{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fee91a2-2121-44ef-81b3-11ffc68db20d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in the Census 2020 pop data -- mapped onto 2020 blockgroups\n"
     ]
    }
   ],
   "source": [
    "#THIS FILE: Ohio legisl HD.  Copy from 1850 -15d\n",
    "\n",
    "from shapely.geometry import Point, LineString, Polygon, box\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ast\n",
    "import time\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/oh_pl2020_bg.dbf\")  #use blockgroups for Katy's COI file\n",
    "tractPopFile.head()  #about 40 sec to load\n",
    "print(\"I read in the Census 2020 pop data -- mapped onto 2020 blockgroups\")\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=2):  #TRY 2 FOR OHIO V2.  USUALLY 4\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):  #TRY MAXLOOPS = 3 FOR OH REDO, NOT USUAL 6\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 3, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave\n",
    "\n",
    "def isRookAdj(geo1, geo2):  #intersection with rook (not queen) adjacency\n",
    "    isRookAdj = False\n",
    "    if geo1.intersects(geo2):\n",
    "        if (geo1.intersection(geo2)).geom_type != Point(0,0).geom_type:\n",
    "            isRookAdj = True\n",
    "    return isRookAdj\n",
    "\n",
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9e2fbed-247e-4a00-be18-5507b7e2b88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 9472 building blocks for OH\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"OH\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "GEOID20 = tractPopFile['GEOID20']\n",
    "nTracts = len(tractPop)\n",
    "tractCP = [tractGeom[t].centroid for t in range(nTracts)]\n",
    "nVTDs = nTracts #nomenclature\n",
    "print(\"there are {0} building blocks for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "countyNo = [0]*nTracts\n",
    "for t in range (0,nTracts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "skipList = [6080, 3774, 9049, 2675, 6268, 8741]\n",
    "for t in skipList : #coastal bgs for Ohio already known\n",
    "    isSkippedTract[t] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a619e262-3e70-4945-87b1-58245cc514c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 1000\n",
      "working on tract 2000\n",
      "working on tract 3000\n",
      "working on tract 4000\n",
      "working on tract 5000\n",
      "working on tract 6000\n",
      "working on tract 7000\n",
      "working on tract 8000\n",
      "working on tract 9000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATE COUNTY-LEVEL MAP OF THIS STATE - excluding lakeshore tracts from county building\n",
    "for s in skipList :\n",
    "    countyNo[s] = -999  #don't count these lakeshore tracts as part of a county\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "lakeErieGeom = []\n",
    "countyPop = [0.]*nCounties\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "countyTractList = [list() for c in range(nCounties) ]\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if t not in skipList:\n",
    "        c = int(countyNo[t])\n",
    "        countyTractList[c].append(t)\n",
    "        nCountyTracts[c] +=1   #found another tract that belongs to this county\n",
    "        countyPop[c] += tractPop[t]\n",
    "        if nCountyTracts[c] == 1:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "countyMAP = countyGeom[0]\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c],8)\n",
    "    countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cda12941-0eef-4a29-a376-dd0d4bc2927b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 districts, each with avgDistrictPop of 119186.343\n",
      "I will now build a map from the counties and display the whole county districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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hhcjIRFHEoiOL4Gvni2faPyN1OWRmcstyMWPvDJzIPIFn2j+DCW0n8BYQTRQDiwHM4fpzosbq5ys/43jmcfyv3/9451uq4lLeJUzeNRkqrQqfD/gcEZ4RUpdEEmJzgQEqA0sjmLKGyKwUqAqwLG4ZBrcYjCifKKnLITOyLXkbxv42FvaW9vj24W8ZVogtLEQknQ/iP4BGp8FrXV+TuhQyA1qdFn/e+BOv7X0NIkQMazkMr0e+3qQu/6d7Y2AxQOWdazU6jcSVEDUeJ7NO4odLP2BO5By42bjpl5enpACCDJbNfSGKInK/XAMLby84DB4sYbVkSrlludh9YzfWnlmL68rrAIC3eryF4cHDpS2M9MxhaAQDiwHkQkVgMcZ8KERUcdXHm0feRDvXdnis9WMAAFGtxtWRI6G6dPmO9zsOe4SBpRHadX0X1p5Zi5O3TkKAgL7+fbE4ejHCXMM4+7EZknpYBAOLASqTpU7USVwJUePw9bmvkZyfjG+GfAO5TA5RFHHl4aEov34dNhERgFaL0pMn9e93HjdOumLJ6JTlSiw+uhi/XvkVkV6RWBy9GF29usLL1kvq0siMMbAYoHLCNbawENVdRnEGPjn5CcaEjkEb1zYVC7VaQF7RklkaHw8A8Jg9C5qMTOSuXQttfoFU5ZKRHU47jHmH5qGovAhLey7FkKAhUpdEDQQDiwEq515hCwtR3f2c/DNKNaU4nnEcn5z8BNG+0Wjn2g4ttvyI3LXrULR/H0qPxyNr6TsAAJuOHWHTgXd2buhK1CX47/H/4ruL3yHSKxJv9niTd1GmGmFgMQAvayYynrFhY+HRzAP7b+7HxnMbserUKjhZOeEBnwfQs19P9Bj/MXwylSj8cxcsWwTCNjoaMkvOaNqQXc67jCd+fQIyQYbXI1/H6JDRnISTaoyBxQDsEiIynmYWzTAseBiGBQ+DRqdBYnYi9qfux4GbB/Db1d8gQEA7t3boGdET0b7uaGvBX1MN2YazG/BOXEVr2ZZHtiDYOVjiiqih4m8CA7BLiMg0FDIFOnl0QiePTnip80u4VXILB24ewIGbB7D+7HqsPLUSzlbO6OHbA9G+0ejh0wNO1k5Sl033odFpcD73PP64/gfi0uOQlJMEAPju4e8YVqhOGFgMYA7XnxM1Be7N3DGi1QiMaDUCGp0Gp26d0geYX678AgEC2ru3R7RvNB70fRBhrmHsWjADGp0G+1P3Y+f1ndibuhfKcqX+tcXRi/Fw0MO8WzzVWZ1+0pcuXQpBEPDKK6/ol/3vf//DQw89BAcHBwiCgPz8/Gq3s2DBAgiCUOURGhpal9KMqvIHjS0sRPVHIVMgwjMCL3d+GZuHbsaux3Zh4QMLYW9pj5UnV+KJX5/AgkMLpC5TEmqtGhnFGZKPq1NpVViTtAaDfxyMl/56Cedyz2FM6BisH7Qeex7fg+PjjmNoy6EMK2QUtW5hiYuLw+rVqxEeXnX0fklJCQYOHIiBAwciNjbW4O21bdsWf/7559+FKcyn8YddQkTSK1GX4GLeRZzKOgWZIENvv94YGzZW6rLqhVanxfm88ziafhTH0o8hISsBpZpS2FnYobVza4S6hKKbdzdE+0bX2w0kD6UdwqIji5BenI4hLYbgybAn/75MnRodcwidtUoFRUVFGDt2LD777DMsWrSoymuVrS179uypWSEKBby8zHPSIP1VQhx0S1Tv9qfux8bzG3Hw5kE4WTlhdMhojA4ZDW87b6lLMxlRFHG14CqOpB/BsYxjiMuIg7JcCRuFDTp7dsakDpMQ4BCAKwVXcCH3Avbf3I+vz38Na7k1nu/wPPoF9EOAQ4BJajuTfQb/O/0/7E7Zja5eXbGi7woEOQaZZF9E/1SrwDJ58mQMGTIEMTExdwSW2rp06RJ8fHxgbW2NqKgoLFmyBP7+/nd9r0qlgkql0j9XKpV3fZ+xVLawSN38StTUXM67jBd3vQig4t4yg1oMqrcWhPqWVpSGo+lH9SEluzQbCpkCHdw7YFzYOER6R6K9W3tYyP+esr4P+ui/P5R2CFsvbcX/Tv8PyxOWw9fOFwEOAfC396/46lDx1cfOB1qdFimFKbihvIEbhTdwXXkdNwpvIKc0B0XqIqi1atgobNDcvjlaOLZAgEMAMoszcTTjKM7nnoe/vT/HplC9q3Fg2bRpExISEhAXF2e0IiIjI7F27VqEhIQgPT0dCxcuRM+ePZGUlAR7e/s73r9kyRIsXLjQaPuvDi9rJpJGkFMQRoeMxrcXvoUAodGFlWJ1MX5J/gXfXfwOF/MuQoCANq5tMLTlUHT36o6OHh0NvlPxAz4P4AGfB1CqKcWhm4dw8tZJXFdeR1xGHH689CPKdeUAKu6NphW1+vVsLWzhb+8Pfwd/hDiHwM7SDpYyS5RoSnBdeR0JWQnYcmkLnKydEOkViefCn0Mfvz76m8IS1ZcaBZaUlBS8/PLL2LlzJ6ytrY1WxKBBg/Tfh4eHIzIyEgEBAfjuu+8wceLEO94fGxuL6dOn658rlUr4+fkZrZ5/472EiKQhE2SYEzkHGp0G8w7Ng0yQYWjLoVKXVStqnRpTd09FelE6fO18oRN1SMhKgEqrwkPNH8KkDpPQ1asrHK0c67QfG4UN+gb0Rd+AvvplOlGHzOJMXC+8jhvKG7CUW+pDiqu1a7WtJDpRBwECW1NIUjUKLPHx8cjKykLnzp31y7RaLfbt24cVK1ZApVJBLq976nZyckLr1q1x+fKdd20FACsrK1hZ1d9fWuwSIpKOIAiYFzUPWlGLOQfmIF+Vj3Fh4xrUyTOlMAWrTq3CwZsHEeMfg8LyQijkCrzQ4QUMbjHY5Df9kwkyeNt5w9vOG929u9dqfSKp1Siw9O3bF4mJiVWWTZgwAaGhoZg1a5ZRwgpQMag3OTkZ//nPf4yyvbrioFsiackEGRY+sBDOVs54N+5dJOcnY2bXmQZ3l0jl9K3TWHFiBY5mHIWjpSNmd5vdZK5sIjK2GgUWe3t7tGvXrsoyW1tbuLq66pdnZGQgIyND3zqSmJgIe3t7+Pv7w8XFBUBF8BkxYgSmTJkCAJgxYwaGDh2KgIAApKWlYf78+ZDL5RgzZkydD9AYOA8LkfRkggzTu0xHoGMglhxdgoNpB/F6t9fR27+31KVVkVuWiz0pe/Dbld9wNOMoAh0CMSdyDoa2HAobhY3U5RE1WEaf7GTVqlVVBsQ++OCDAIA1a9bg6aefBgAkJycjOztb/57U1FSMGTMGOTk5cHd3R3R0NI4cOQJ3d3djl1crnIeFyHyMbDUS3by6YdHRRXjpr5fQ3bs7ngh5Ar38ekEhM/78TaIoIqUwBXEZcTh56yQcLR2hgw6WMktYK6zhauMKrU6LjOIMpBWnYU/KHpRpytDRoyOW9VqGGP8YDlAlMgJBbAQDM5RKJRwdHVFQUAAHBwejb79cW46IDRFYHL24wQ74I2psRFHEzus7se7MOpzOPg0PGw+MaDUCo1qNqvMcLUXlRdidshuH0g4hLiMOWSVZ+tcCHAIgE2RQa9Uo0ZQgX5UPGWTwaOYBL1svtHVri6faPGXycSlE9WnETyPQ3bs7ZnWbZdTt1uT8bT7TyZoxdgkRmR9BENA/sD/6B/bH+dzz2HxhM9afXY/PEj9DT9+eeKz1Y4j2jTa4daNMU4Z9qfvw+9XfsS91H8p15Wjj2gaDWwxGV6+u6OTRCfaWd06zoNVVXCLMVhQi02JgMYDs9i2XOOiWyDyFuoRibtRcvNrlVfx29Td8d+E7TNk9BV62XhjZaiSGtBgCXzvfO0KFWqfG0fSj+P3q79h1YxeK1cUIcwnD1E5TMbDFQINaSRhUiOoHA4sB9BPHNfzeM6JGrZlFMzza+lE82vpRnMk+g80XN2NN0hqsPLkSFjIL+Nr5wsvWC242bpALcuxL3Yc8VR4CHQIxvs14DGoxCIGOgVIfBhHdBQOLAThxHFHD09atLdq6tcWrXV7FqVun9FPRZ5VkIb04HcXqYgwPHo5BLQYh1CW0Qc3rQtQUMbAYQBAECBDYJUTUANlb2iPaN1rqMogaPKnPgZy+0ECCILCFhYiISCIMLAaS8Z+KiIhIMjwLG0gQhCp3OCUiIqL6w8BiIAECrxIiIiKSCAOLgQSBg26JiIikwsBioMpLm4mIiKj+MbAYSBDYJURERE2TOcxTxMBSA+wSIiIikgYDi4E46JaIiEg6DCwG4qBbIiIi6TCwGIgtLERERNJhYDEQ7yVERERNmdR/tDOwGErgoFsiIiKpMLAYiF1CRERE0mFgMRAH3RIREUmHgcVAnOmWiIhIOgwsBmKXEBERkXQYWAzELiEiImqqzKGXgYGlBtjCQkREJA0GFgNxHhYiIiLpMLAYiF1CRERE0mFgMZBQMXMcERERSYCBxUDmMOCIiIhIKlL3MjCw1IDUHxYREVFTxcBiKN5LiIiISDIMLAbixHFERETSYWAxEK8SIiIikg4Di4HYwkJERE2VOVx4wsBiIHP4sIiIiJoqBhYDsUuIiIhIOgwsREREZPbqFFiWLl0KQRDwyiuv6Jf973//w0MPPQQHBwcIgoD8/HyDtvXJJ58gMDAQ1tbWiIyMxLFjx+pSmklwDAsREZE0ah1Y4uLisHr1aoSHh1dZXlJSgoEDB+L11183eFvffvstpk+fjvnz5yMhIQEdOnTAgAEDkJWVVdvyjI43PyQiIpJOrQJLUVERxo4di88++wzOzs5VXnvllVcwe/ZsdO/e3eDtvf/++/i///s/TJgwAW3atMGqVavQrFkzfPnll7UpzyQ4hoWIiEg6itqsNHnyZAwZMgQxMTFYtGhRnQooLy9HfHw8YmNj9ctkMhliYmJw+PDhu66jUqmgUqn0z5VKZZ1qMARvfkhkep/vv4LUvFIIQsXP3JcHr6K9ryN6BLtBLgPkggCZTPj7q0yATABkQsX3Fc9vf69/T8XrMkGAcPtiv8qr/v5+Xvm9gKggVzg2s6jfAy8vAW6dAwQZIMgBmfwfX2X/ev6P5dryiucWNrfXFSqO5m7fC7zSkepG6mERNQ4smzZtQkJCAuLi4oxSQHZ2NrRaLTw9Pass9/T0xPnz5++6zpIlS7Bw4UKj7N9QbGEhMi2tTsSiX88BAFp72kF3+8ct8WYBCkrV0OpE6MSKh1aH219F6HQitLe/F0Xov6+tqX2C8Wr/EGMckuH+ehs4vKIediTcJcjI/l5e5XvhHsv/1TD/75OYhTUwbgvgFlwPx0NNSY0CS0pKCl5++WXs3LkT1tbWpqqpWrGxsZg+fbr+uVKphJ+fn0n3yYnjiEyr8u//dx8Nx+Nd6v7z/M8gUxF0KpaL4t9/euh/pG9/HbHyIErLtXXed42pSwHXVsCozwFRC+h0t79q//X1X8uvHawo3i+y4mBEXcVzg78Xb3+vM/B7VHy9o7Xm9nNVAXDoYyD/GgMLGV2NAkt8fDyysrLQuXNn/TKtVot9+/ZhxYoVUKlUkMvlNSrAzc0NcrkcmZmZVZZnZmbCy8vrrutYWVnBysqqRvsxBrawEDUcMpkAGQRY1OBXkkIuQCvFHyYyBSC3BHw61my9NsNMUk6tFdysCCxEJlCjQbd9+/ZFYmIiTp48qX906dIFY8eOxcmTJ2scVgDA0tISERER2LVrl36ZTqfDrl27EBUVVePtmYrA/l8ikzKHPwdkggBdHbqTak1uAejU9b9fUzGHD5OMyhzOgTVqYbG3t0e7du2qLLO1tYWrq6t+eUZGBjIyMnD58mUAQGJiIuzt7eHv7w8XFxcAFcFnxIgRmDJlCgBg+vTpGD9+PLp06YJu3brhww8/RHFxMSZMmFDnAzQmtrAQNW4KuQCNFIFFpgC0jSGw3P63M4OTGzU+tbpK6H5WrVpVZUDsgw8+CABYs2YNnn76aQBAcnIysrOz9e8ZPXo0bt26hXnz5iEjIwMdO3bE9u3b7xiIKyWOYSEyrcqfLylPdXJBgE6qLiGdBGNnjE3UVXxlYCETqHNg2bNnT5XnCxYswIIFC+67zrVr1+5YNmXKFH2LiznizQ+JGj+ZTKjTFUa11li6hPRhj78vyfh4LyED8bJmosZPLgjQ6iTYsUzOLiGiajCw1AC7hIhMR/+3uYQnO5lMqi4hC6Aku/r3mTu2sJAJMbAYiC0sRI1fRQuLBD/n1o4VX4tz6n/fRsUWFjIdBhYDcdAtkWmZw4+XXCbRPCx+3Sq+5lyq/30bE1tYGjWp/2hnYDEQ79ZMVD+kPNXJZBLNw6IvwOgXbtYvkS0sZDoMLAYyh0lziMi05AKk6RLSlld8beiBRd8lxFMLGR//VxGRWagc7Crl3wZyqQbdajW3C7Cs/30bE7uEGi1zmNqDgcVAHMNCZFrq29cTW8il+7Wk0YmQyyT4xVzZwtLQAwsH3ZIJMbDUAMewEJmORlvx8yVVYClTa7Hnwi1puoR0t1tYZA38VzJbWMiEGvhPR/3hZc1EpqXWVbawSHOyU5ZWTNz257ksJKYWSFJDgz/Rc2p+MiEGlhowhz48osaqsmVDIVELi4eDNcZHBQAAhq44gE5v/oEvD1yVpJaGiy0sZDoMLAYSRZGBhciEKruEFFKMIblt4bB22DalB+Y93AZ5JWq8+ctZ/dgaMgAvayYTYmAhIrOg0UkfWAAgvLkTnolugQVD2wAA/vvHxXrYa2M50bOFhUyHgaUGOBcLkelob49hUUg0huXfnu7RAgGuzZBeUCp1KQ0HW1jIhBhYiMgsqG93CcnN6EqZ5s420lw11GAxsJDpmM9vBiJq0rRm0iX0TzKhniaSayyXA1deJdTQj4PMEgNLDXDiOCLT0Y9hMZMuIeD2zRDZwmI4dgmRCTX0G1fUG5kggw68WoDIVDS3r8YxpxYWAUBGQRm2J6XDSiGHlUKGYE87eNhbS12aedLP2GslbR3UKDGwGIhT8xOZ1t9XCZlPw6+vsw3+unALL2xI0C+LbOGCb5+PMs0OG3rLhEZV8VXR0G8xQOaIgcVAnOmWyLR0twPLuC+OwtpCDoVMgEIuQC6TQSETIJcJsLj93EIm4D9RAXgoxMOkNb01rB3mDG4DlUaLco0Oi387h+RbxSbYUyP53cIWFjIhBhYDCQJbWIhMqaO/E17t1xpF5RpotCK0OhEanQ4arQiNruK5WquDVifi96QMuNpZmjywCIIAG0s5bCzlAAB7awuOabmfRnMTRzJHDCwGEiBAJ3IMC5GpNLNUYGrfVga9d/TqwyjX1P/Po0yAia8aauBdQpU3cZTz1ELGZz6dxWZOhMiJ44jMhKVChnIJpsyvaGmt9902HPq7TjOwkPHxf5WhRN78kEhqu89n4oeEm9h/KRsxYabtDrobmSBAa5LEcvt3y7qHAZnFXV6WVQzIFWR3fo9/LpdVfY9+/crvK7/ePoZ/Hoso3mW5aNj3leuU5lZ8z8BCJsD/VQZiCwuR9LadTMO+C7fQPcgFMWGe9b5/ucxEXULNuwAPzgQ0d7kNQOX+RN3th/iP73X3X16xYtXtQERFyPlHiKnx97j3exybAxY2dfjHILo7BhYD8W7NRNKzUsgR5GGHTc+Z6LLiashM1SVkZQ/0mWOCDRM1HhzDYiBe0kwkPRtLORJT89Fh4R94ZdOJet+/IHDmW2o6MoozsOHsBkzeNRnncs9BIXFXH1tYDMQuISLpTegRCC9Ha/x1PgunUwvqff8m6xIiqmdanRYJWQlIyk7CyayTyFfl4/GQxzG4xWCkFqZiadxS7EvdB4VMgW5e3fBihxfxWMhjktbMwGIgdgkRSS/A1RYv9GoJZakav5xOr/f9y9nCQo1AVkkWZu+fjbiMONgobNDerT0s5BaYvX82VpxYgVult+Bq7Yq3o9/GQ34PwcHSQeqSATCw1AgDC5F5UEh0U0ILuQxqLQMLNVwpyhT8387/g1qrxqcxn6K7d3d9V09cRhx2XNsBXztfjA4ZjWYWzSSutioGFgOxS4jIfMhlMqglmIfFQiHNfomMYevlrVh6bClcrF2wfvB6+Nj5VHm9q1dXdPXqKlF11eOgWwNxWn4i86GQS9nCwsBCDU9cRhzmHpyLPn598N3D390RVhoCBhYDieAYFiJzIZcJ+rs71ydLucDAQg1STlkOAMDT1hN2lnYSV1M7DCwGYpcQkflQyARo6jk4FKs0KFJpodaKbHGlBqe3X28AwOeJn+OdY+80yP/DHMNiIFEUIROY74jMgUImoEStxciVB2FtIYcg3J7oVaz446LiKypmkP/Hc1EUb3+tfP2fz8V/bOPvbuDicg2yC8tRqtYCAKwtZNCJgJx/v1ADYiW3wumnTuOb899gybElcLZ2xnPhz0ldVo3UKbAsXboUsbGxePnll/Hhhx8CAMrKyvDqq69i06ZNUKlUGDBgAFauXAlPz3tPo/30009j3bp1VZYNGDAA27dvr0t5RscuISLz0K+tF67llKBYpdEHCUGo+AkVbs8cX/m84sWKn9+/X/vHc6HiDXd9DYCNpQJudpZwt7eCm50VWrjZQi7j7wJqeARBwJNhT0JZrsTHJz5GUnYSPnjoA8hlcqlLM0itA0tcXBxWr16N8PDwKsunTZuGX3/9FZs3b4ajoyOmTJmCkSNH4uDBg/fd3sCBA7FmzRr9cysrq9qWZhINsfmMqLHydbLBgkfaSl0GUYP0fPjzECFi5cmV+O3qbxjacqjUJRmkVn0cRUVFGDt2LD777DM4OzvrlxcUFOCLL77A+++/jz59+iAiIgJr1qzBoUOHcOTIkftu08rKCl5eXvrHP7drDjiGhYiIGgNBEDCpwyREeEbg1yu/Sl2OwWoVWCZPnowhQ4YgJiamyvL4+Hio1eoqy0NDQ+Hv74/Dhw/fd5t79uyBh4cHQkJCMGnSJOTk5NSmNJPhVUJERNSYRPtG42DaQRSri6UuxSA1DiybNm1CQkIClixZcsdrGRkZsLS0hJOTU5Xlnp6eyMjIuOc2Bw4ciK+++gq7du3CO++8g71792LQoEHQarV3fb9KpYJSqazyMDWdqINcaBj9fERERNXp49cHAPDYz9LeI8hQNQosKSkpePnll7Fx40ZYW1sbrYgnnngCjzzyCNq3b4/hw4fjl19+QVxcHPbs2XPX9y9ZsgSOjo76h5+fn9FquReNTiP5nSqJiIiMJcgpCP0C+iGlMAX/O/0/sx+rWaPAEh8fj6ysLHTu3BkKhQIKhQJ79+7FRx99BIVCAU9PT5SXlyM/P7/KepmZmfDy8jJ4P0FBQXBzc8Ply5fv+npsbCwKCgr0j5SUlJocRq1odJoGM5KaiIjIEMt6LcOkDpPw8YmPsS15m9Tl3FeNAkvfvn2RmJiIkydP6h9dunTB2LFj9d9bWFhg165d+nUuXLiAGzduICoqyuD9pKamIicnB97e3nd93crKCg4ODlUepqbRaaAQ2MJCRESNh0yQ4cWOLyLQIRAnsk5IXc591egMbG9vj3bt2lVZZmtrC1dXV/3yiRMnYvr06XBxcYGDgwOmTp2KqKgodO/eXb9OaGgolixZghEjRqCoqAgLFy7EqFGj4OXlheTkZMycORPBwcEYMGCAEQ7ROLSili0sRETU6CRkJuCa8hqe7/C81KXcl9GbDD744APIZDKMGjWqysRx/3ThwgUUFBQAAORyOU6fPo1169YhPz8fPj4+6N+/P9566y2zmotFq9Ny0C0RETUqpZpSzD04Fx3dO2JQ4CCpy7mvOgeWfw+Mtba2xieffIJPPvnknuv8c2CPjY0NduzYUdcyTE4HHafmJyKiRmXjuY3IKM7AJ30/MfteBJ6BDcTLmomIqLG5WXQTwc7BCHQMlLqUajGwGEir07KFhYiIGhWVRgVZA4kCvOzFQGxhISKixkKj02Bf6j78fOVnuFi7SF2OQRhYDMR7CRERUWOx4NAC/JT8EwBgftR8iasxTMNoBzIDgiCY/SyAREREhrC1sAUABDgEoFxXLnE1hmFgMZAMMuigk7oMIiKiOpvVbRY+7/85/Oz98Nre13Cl4IrUJVWLXUIGkgky6EQGFiIiavhkggyR3pEo15bjwM0D0OnM//zGFhYDsUuIiIgam0Nph+DZzBMtnVpKXUq1GFgMJEBgCwsRETUqXrZeyC3LRWZJptSlVIuBxUAyQQYRbGEhIqLGY2SrkbC3tMdHCR9JXUq1GFgMxBYWIiJqbOwt7fGfNv/BL1d+QYm6ROpy7ouBxUAaUQOFjGOUiYio8dCJOsRnxsPfwR/WCmupy7kvBhYDqXVqWMgspC6DiIjIaL4+9zUO3jyIVyNeNfvbz5h3dWZCFEVodBoGFiIiajREUcTq06sxqvUo9PbvLXU51WJgMYBGpwEAWMgZWIiIqHEo1ZQiX5WPLp5dpC7FIByUYQC1Tg0AbGEhIqJGIb8sHytPrQRQMT1/Q8DAYgAGFiIiagyU5Up8dvozfHvhW4iiiGkR09DOrZ3UZRmEgcUADCxERNTQaXVavLDzBSTnJ2Ns2FiMCxsHVxtXqcsyGAOLAdRaBhYiImrYNl/cjKTsJKwfvB4d3DtIXU6NcdCtAfQtLBx0S0REDdTaM2sxtOXQBhlWAAYWg1QGFk4cR0REDVFuWS5uFt1ElE+U1KXUGgOLATiGhYiIGrKd13YCALp5dZO4ktpjYDEAx7AQEVFDtfvGbiw7vgxDg4bCo5mH1OXUGgOLAdjCQkREDdH3F7/HK3+9gmjfaMyNmit1OXXCQRkGYGAhIqKGJiEzAW8feRuPtX4Mc7rPMft7BVWnYVdfT1RaFQDAUm4pcSVERETVyyvLw7Q909DBowNmR85u8GEFYAuLQSrHsDCwEBFRQ/Dm4TeRW5aLbx/+ttH0DjT8yFUPKltYrORWEldCRERUvUJ1IXr49oCXrZfUpRgNA4sBynXlADhxHBERNQy5Zblobtdc6jKMioHFAOXacsgEGRQCe9CIiMj8eTbzxM2im1KXYVQMLAYo15bDUmYJQRCkLoWIiOi+RFGEpcwSBaoCqUsxKgYWA5TrytkdREREZu9k1kmM+20cdqfsxsNBD0tdjlExsBhApVVxwC0REZm13678hgnbJ0AravFZ/8/wZNiTUpdkVByUYQC1Vt1oLgsjIqLG6eMTHyPQMRDrB69vlOcstrAYQK1Tcw4WIiIya9523rCWWzfaC0QYWAyg1rGFhYiIzNt/wv6DpJwknM09K3UpJlGnwLJ06VIIgoBXXnlFv6ysrAyTJ0+Gq6sr7OzsMGrUKGRmZt53O6IoYt68efD29oaNjQ1iYmJw6dKlupRmVOwSIiIic9fevT0AILUwVeJKTKPWgSUuLg6rV69GeHh4leXTpk3Dzz//jM2bN2Pv3r1IS0vDyJEj77utd999Fx999BFWrVqFo0ePwtbWFgMGDEBZWVltyzOqcl05u4SIiMisbb6wGQpBgQ7uHaQuxSRqFViKioowduxYfPbZZ3B2dtYvLygowBdffIH3338fffr0QUREBNasWYNDhw7hyJEjd92WKIr48MMP8cYbb2DYsGEIDw/HV199hbS0NGzdurVWB2Vs7BIiIiJzJooi/kr5C338+zSq6fj/qVaBZfLkyRgyZAhiYmKqLI+Pj4dara6yPDQ0FP7+/jh8+PBdt3X16lVkZGRUWcfR0RGRkZH3XKe+sUuIiIjMWU5ZDs7lnkNf/75Sl2IyNR5KvGnTJiQkJCAuLu6O1zIyMmBpaQknJ6cqyz09PZGRkXHX7VUu9/T0NHgdlUoFlUqlf65UKmtyCDXGLiEiIjJnlTfptbO0k7gS06lRC0tKSgpefvllbNy4EdbW1qaqqVpLliyBo6Oj/uHn52fS/bFLiIiIzJmPrQ8sZZa4rrwudSkmU6PAEh8fj6ysLHTu3BkKhQIKhQJ79+7FRx99BIVCAU9PT5SXlyM/P7/KepmZmfDyunufWuXyf19JdL91YmNjUVBQoH+kpKTU5DBqTKPVcGp+IiIyWzpRh3JdOZopmkldisnUKLD07dsXiYmJOHnypP7RpUsXjB07Vv+9hYUFdu3apV/nwoULuHHjBqKiou66zRYtWsDLy6vKOkqlEkePHr3nOlZWVnBwcKjyMKVyXTlbWIiIyCxdyL2AiX9MBAC42bhJXI3p1GgMi729Pdq1a1dlma2tLVxdXfXLJ06ciOnTp8PFxQUODg6YOnUqoqKi0L17d/06oaGhWLJkCUaMGKGfx2XRokVo1aoVWrRogblz58LHxwfDhw+v+xEagUangULWOGcOJCKihutczjmM/W0sfO188WnMp4j2jZa6JJMx+ln4gw8+gEwmw6hRo6BSqTBgwACsXLmyynsuXLiAgoK/b3s9c+ZMFBcX47nnnkN+fj6io6Oxfft2ScfJ/JNO1EEmcFJgIiIyL/tS90EhU+CHR35o9BeHCKIoilIXUVdKpRKOjo4oKCgwSffQ4z8/jnD3cLzR/Q2jb5uIiKi2dt/YjZf/ehm/jfgNfg6mvQDFFGpy/mY/hwF0og4CBKnLICKiJu70rdP4K+Uv2FrYItIrEpHekVDIFNh5YyeeafeM1OWZFAOLAXTQQS6TS10GERE1YTmlOZiwfQJsLWyh0WmwPGE5wt3CYSW3wpG0IwwsVDHlMVtYiIhIShklGSjXlePjnh8j0jsSe1P34odLP8Bb443xbcdLXZ7JMbAYQCtqOeiWiIgkFeocilCXULx55E2sG7gOffz7oI9/H6nLqjc8CxtAFEXIBXYJERGRdOQyOT7q/RF0og6v7XtN6nLqHQOLAXSiDoLALiEiIpKWt503ng9/HieyTiCj+O7322us2CVkAHYJERGR1K4UXMGnJz/F9mvb4WrtChdrF6lLqlcMLAYQRZGBhYiIJPNu3LtYf3Y97C3s8YDPA1jac2mjnyju3xhYDKADZ7olIiJpiKKI9WfXY1jLYZgXNa/JBZVKPAsbQKfTQcZ/KiIikkDlGMr04vQmG1YABhaDsIWFiIik1D+gP0Q0+Dvp1AnPwgbgzQ+JiEhKmSWZ8Lb1lroMSfEsbABe1kxERFIoLC/E20fexqlbp9DOrZ3U5UiKg24NoBN1nDiOiIjqjVqnxpZLW7Dy5EqUaEowo8sMPN76canLkhQDiwF4WTMREdWXAlUBJuyYgMt5lzEkaAhe7vwyvGy9pC5LcgwsBtCKWt78kIiI6sXGcxtxKe8SNg3ZhLZubaUux2yw2cAAIkTIZewSIiIi0wtyDAIAnMk5I3El5oWBxQA6UccWFiIiqhcDAgdgTOgYvHXkLbRf1x63Sm5JXZJZYGAxAO8lRERE9UUQBMR2i8Xz4c8DAP7vj/+DVqeVuCrp8SxsAA66JSKi+iQIAqZ0moIPe3+Iq8qrWHZ8mdQlSY5nYQOwhYWIiKTQ2683BgQMwG9Xf5O6FMnxKqFqiGLFVMgMLEREVF9uldzC+/HvY2/qXhSWFyLGP0bqkiTHwFINnagDwMBCRET1o0xThid/exJqrRrjwsahi2cXdPbsLHVZkmNgqQYDCxER1afUwlRkFGdgVcwq9PDtIXU5ZoNn4WrowMBCRET1RyNqAACOVo4SV2JeeBauhr6Fhf9URERUDyovYa4cQ0kVeBauBruEiIioPh3PPA4LmQWCnYOlLsWs8CxcDQYWIiKqL6Io4tcrvyLaNxo2ChupyzErHHRbDQYWIiIyhXJtOS7lXUKhuhAWMgs4Wjpi5/WdOJd7DtMipkldntlhYKlGZWARBN5LiIiI6k6tU2NN0hqsSVqDInVRldcECBjVahSifKIkqs58MbBUozKwyAXerZmIiOrmRNYJLDqyCMn5yRgbNhYDAwfC2doZKq0KuWW5aO3cmlcH3QMDSzVEcKZbIiKqm9yyXHwQ/wG2Xt6Kdq7t8PWQr9HGtU2V97RES4mqaxgYWKpReXmZAHYJERFRzW2/th2LjiyCKIqYFzUPo1qN4h/BtcDAUo3KFhZ2CRERUU1kl2bjw/gP8VPyT+gf0B9zus+Bi7WL1GU1WAws1eCgWyIiqolSTSk+T/wc68+uh0KmwMIHFmJE8AieR+qIgaUaWrGiS4jNd0REVJ1bJbcwbc80nM89j7FhY/FMu2c4iNZIanQW/vTTTxEeHg4HBwc4ODggKioKv//+u/715ORkjBgxAu7u7nBwcMDjjz+OzMzM+25zwYIFEAShyiM0NLR2R2MClVMjM7AQEdG9aHQabDi7AUO3DkVKYQq+GPAFpkVMY1gxohqdhZs3b46lS5ciPj4ex48fR58+fTBs2DCcOXMGxcXF6N+/PwRBwO7du3Hw4EGUl5dj6NCh0Ol0991u27ZtkZ6ern8cOHCgTgdlTJw4joiI7ie3LBcv/PkC3o17Fw8HPYxtw7ehg3sHqctqdGrUJTR06NAqz99++218+umnOHLkCG7evIlr167hxIkTcHBwAACsW7cOzs7O2L17N2JiYu5dhEIBLy+vWpRvegwsRER0L3EZcZi1bxa0ohZfDPgCXb26Sl1So1Xrs7BWq8WmTZtQXFyMqKgoqFQqCIIAKysr/Xusra0hk8mqbTG5dOkSfHx8EBQUhLFjx+LGjRu1Lcvo1Do1AEAhcLgPERFVEEURa5LW4Nk/nkWgYyC+H/o9w4qJ1fgsnJiYiKioKJSVlcHOzg5btmxBmzZt4O7uDltbW8yaNQuLFy+GKIqYPXs2tFot0tPT77m9yMhIrF27FiEhIUhPT8fChQvRs2dPJCUlwd7e/q7rqFQqqFQq/XOlUlnTwzCYRqcBAFjILUy2DyIiajhK1CWYf2g+tl/bjontJmJqp6mQyzj1hanVuIUlJCQEJ0+exNGjRzFp0iSMHz8eZ8+ehbu7OzZv3oyff/4ZdnZ2cHR0RH5+Pjp37gyZ7N67GTRoEB577DGEh4djwIAB+O2335Cfn4/vvvvunussWbIEjo6O+oefn19ND8NgbGEhIqJKGp0GL/31Evam7sV/e/0Xr0S8wrBST2p8Fra0tERwcDAAICIiAnFxcVi+fDlWr16N/v37Izk5GdnZ2VAoFHBycoKXlxeCgoIM3r6TkxNat26Ny5cv3/M9sbGxmD59uv65Uqk0WWipbGFRyBhYiIiauo8SPsLxjOP4rP9n7AKqZ3UeSarT6ap0zwCAm5sbnJycsHv3bmRlZeGRRx4xeHtFRUVITk6Gt7f3Pd9jZWWlv7S68mEqGpGBhYiIgB8v/Yg1Z9ZgesR0hhUJ1OgsHBsbi0GDBsHf3x+FhYX4+uuvsWfPHuzYsQMAsGbNGoSFhcHd3R2HDx/Gyy+/jGnTpiEkJES/jb59+2LEiBGYMmUKAGDGjBkYOnQoAgICkJaWhvnz50Mul2PMmDFGPMza049hkXEMCxFRU3Xg5gG8dfgtPNb6MfynzX+kLqdJqlFgycrKwlNPPYX09HQ4OjoiPDwcO3bsQL9+/QAAFy5cQGxsLHJzcxEYGIg5c+Zg2rRpVbZR2WVUKTU1FWPGjEFOTg7c3d0RHR2NI0eOwN3d3QiHV3dq7e0xLGxhISJqEvLK8vDHtT9wTXkNqYWpuF54HVcLrqKnb0/ERsZyin2JCGLlVK4NmFKphKOjIwoKCozePbTz+k5M3zMdB544wBkLiYgauZ8u/4Q3D78JHXTwt/dHc/vm8LXzRWePzugX0I8DbI2sJudvNhtUg11CRERNw+aLm/Hm4TcxPHg4pkdMh7O1s9Ql0T8wsFSDVwkREUnjSsEVfH3ua2SXZsPJygk9fXuit39vk8w8/s35b7D46GI8GfokZnebzW4fM8SzcDUYWIiI6leJugSLjy7GtuRtcG/mjlbOrXDq1in8cOkHBDsFY37UfHT06Gi0/R1OO4zFRxfjqTZPYUaXGQwrZopn4WqodWrIBTnvJUREVA9uFt3ES7tfQmphKl6PfB0jW42EpdwSAHAi6wSWxS3DhB0T8EnfT/CAzwMGb1cURRzPPI5Tt07hasFVnM89D2u5NYKdg3El/wqcrZwZVswcA0s11Do1W1eIiExMFEUcuHkAcw7Mga2FLTYO3ohg5+Aq7+nk0QlrB63FxB0TsfrUaoMCS4m6BLtTdmPLpS04lnEM9hb28HfwR3u39lDr1EjKTkK5thyvR77OsGLmeCauhk7UQS5wVDgRkSnoRB2OZRzDpyc/RUJWArp7d8d7D74HJ2unu77fQmaBx0MeR+z+WKQUpsDP/u6znOeW5eKLxC+w+eJmlGpK0dG9Iz7s/SH6+PVhMGmgGFiqoRN17A4iIjIyURTx69VfsTxhOTKKMxDmEoZP+n6Cnr49qw0Uff37wtbCFlsvb8XUTlOrvFagKsCapDX45vw3EAQB49uOx/Dg4fC18zXl4VA9YGCphk7UMY0TERmRVqfF0mNLsenCJkR5R2Fpz6Xo5NHJ4D8ObRQ2GNVqFNYkrYEAAR7NPCCKIi7lX8L2a9tRri3HE6FPYELbCbw0uRFhYKkGu4SIiIzrg/gP8N3F7zA/aj4ebf1orbbxcueXUaIpwcZzG1GsLoZMkMHXzhdDg4ZiYvuJcLNxM3LVJDUGlmqIENklRERkRHtS9+Dx1o/XOqwAgKXcEvOj5mN+1PyKlnAIbA1v5BhYqqHVaSGAPwRERMYgiiJuldyCj52P0bbJPyqbBn7K1dCBXUJERMaSXpyOEk0JghyDpC6FGhgGlmqIoshmRiIiIzmXcw4AEOYaJnEl1NCwS6gavKyZiOj+1Do10ovSkafKg4XMAtYKa3g184KV3KriPkDWTrCSWwEAriqvwt7SHu427hJXTQ0NA0s1GFiIiO5OpVXhkxOf4LuL36FYXXzH6wqZAhqdBjJBhijvKER6R+J4xnG4WLuw5ZpqjIGlGgwsRER30ok6TPtrGo6kH8HTbZ9GV6+ucLVxhUanQYm6BOnF6VCWK+Fr54v04nRsv7odq06tQqmmFK92eVXq8qkBYmCphg4MLERE/3Yu5xz239yP9x96H/0C+lX7/jGhY6DVaVGuK4eNwqYeKqTGhoGlGjqdjpc1ExH9S+VNYZ2snAxeRy6Tw0bGsEK1w6aDavCyZiKiOwU7BcNGYYM/rv0hdSnURLCFpRq8rJmIqEKJugTfX/wep7NPI/FWIko1pfC285a6LGoiGFiqoRW1HMNCRARg/qH52HVjF9q7tUffgL7o6dsT3b27S10WNREMLNXgzQ+JiICrBVex/dp2zO0+F4+HPC51OdQEMbBUg11CRNSUlWnKsPTYUuy4tgMeNh4Y1GKQ1CVRE8XAUg0ddJBxbDIRNVHbkrfhh0s/YFKHSRjZaiTsLe2lLomaKJ6Jq8GJ44ioKQtwCAAAhLqEwsvWS+JqqCnjmbga9wssoiiiQFWAcm15PVdFRFQ/unl1AwC8/NfLEldCTR0DSzXuFVj2p+7H0K1DEb0pGg988wCe++M57E/dL0GFRESmIwgCJnWYBAC4WXRT4mqoKWNgqYZO1N0x6Hbr5a2YvGsyfGx98N6D72Fqp6ko1ZTixV0v4r/H/wtRFCWqlojI+Nq4tgEAXM67LHEl1JRx0G01/n1Z85ZLWzD/0HyMaj0Kc7vP1be+PNXmKWw8txHvxL0DAJgeMd0kVxclZCbglyu/oFhdjDaubTCi1Qg4WDoYfT9ERJVaOrYEAN4DiCTFwFINnahDXEYc3jn2DuIz43Eu9xwea/0Y3uj+RpWuIkEQMK7NOMgEGZYcWwILmQVe6vyS0eoQRRHLE5bji6Qv4G/vD1cbV+y6sQvrz67H2oFr0dy+udH2RUT0Tw5WFX8UFZQXSFwJNWUMLNWwkltBhIid13ci0jsSE9pNwIDAAfcciPtk2JMo15bjv/H/RWpRKiI8IuDezB3dvLrBztKu2v0dTT+Kv1L+gqu1KwYHDYavnS+yS7Ox6tQqfHvhW7wa8SrGtx0PQRCQUZyBiTsmYsquKfjhkR8gl3GCOyIyvhvKGwAANxs3iSuhpkwQG8GAC6VSCUdHRxQUFMDBwbjdIwWqAqQWpSLUObRGgeC7C99hw7kNuKG8Aa2ohZuNG2Z0mYHBLQbftatIrVNjxYkV+DLpS/ja+SKvLA8lmhLYWtiiWF0Ma7k1ZnSZgdGho6us98e1P/Dq3lex89GdvOSQiOqkRF0Cnair8sfVlYIrmL1vNjJLMvHnY3/CQmYhYYXU2NTk/M0Wlmo4WjnC0cqxxus9HvI4Hg95HDpRh9TCVHyY8CFm75+NrZe3YmzYWPjZ+6FMW4bEW4lIzE5EfGY8MoozMD1iOsa3HY8yTRl23diFnNIceNp6ItI7Ei7WLnfsp/LW7sXq4roeKhE1YQdvHsRLu19Cua4cYS5haO/WHmnFaTh48yB87Hywut9qhhWSFAOLickEGfwd/PH+Q+9jf+p+LDu+DFN3T9W/rhAUCHEJQU/fnnik5SNo794eANDMohmGthxa7fYr75SaWZKJlk4tTXMQRNTonc4+DQB4O/ptHLh5AKezT8PJyglvdH8Dw4OHw1JuKXGF1NQxsNSjns17Ito3GqlFqcguzYZMkCHEOQTWCutab9PH1gcAMPfgXPz56J+87xER1YpXMy9oRA3aubXDIy0fkbocojtwDEsjEL4uHCJE2Chs8GjrR+Fg6QAXaxc4WTnBzcYNbVzb1CkUEVHjV6Ypw6M/V/z+WNl3JZysnaQuiZqAmpy/azRx3Kefforw8HA4ODjAwcEBUVFR+P333/WvJycnY8SIEXB3d4eDgwMef/xxZGZmVrvdTz75BIGBgbC2tkZkZCSOHTtWk7KavFNPncKSnkvQw6cH9qXuw6bzm7D46GK8uvdVjN8+Hj2+6YFndzyL9WfXQ1mulLpcIjJD1gprLI5ejOvK63hk6yP49cqvnASTzEqNWlh+/vlnyOVytGrVCqIoYt26dXjvvfdw4sQJBAYGIjw8HB06dMDChQsBAHPnzkVaWhqOHDkCmezu2ejbb7/FU089hVWrViEyMhIffvghNm/ejAsXLsDDw8Ogupp6C8vd6EQdCssLkVGcgbiMOBxKO4Qj6UdgJbfCzK4zMTx4OLuPiBqYLZe24OvzX+Nm0U2UqksR4hKCV7u8iq5eXY22j+zSbCw9thQ7ru1AD98eWBq9lK0tZDI1OX/XuUvIxcUF7733Hvz8/DBo0CDk5eXpd1pQUABnZ2f88ccfiImJuev6kZGR6Nq1K1asWAEA0Ol08PPzw9SpUzF79myDamBgMcytkltYnrAcPyX/hKFBQ/FmjzehkHEYE1FDcOjmITz/5/Po49cHHT06wlJuid+v/o4zOWfwv37/M2poAYA9KXvwxsE3EOUdhfd6vWfUbRNVMlmX0D9ptVps2rQJxcXFiIqKgkqlgiAIsLKy0r/H2toaMpkMBw4cuOs2ysvLER8fXyXMyGQyxMTE4PDhw/fct0qlglKprPKg6rk3c8ei6EVY2nMpfr/6Oz4+8bHUJRGRgb46+xXauLbBh70/xIR2EzA2bCzWDFyDcLdwLD221Oj7e8jvITzb7llsv7YdJeoSo2+fqKZqHFgSExNhZ2cHKysrvPDCC9iyZQvatGmD7t27w9bWFrNmzUJJSQmKi4sxY8YMaLVapKen33Vb2dnZ0Gq18PT0rLLc09MTGRkZ96xhyZIlcHR01D/8/PxqehhN2pCgIXix44tYe2Yt0orSpC6HiO6jTFOGtUlrcTDtIMaFjavSlWshs8BTbZ/CxbyLeOHPFzD+9/EYuW0kpu+ZjmPpdRsLeF15HV8mfYkgxyDeQ4jMQo0DS0hICE6ePImjR49i0qRJGD9+PM6ePQt3d3ds3rwZP//8M+zs7ODo6Ij8/Hx07tz5nuNXais2NhYFBQX6R0pKilG33xRUTmp3PPO41KUQ0V3cLLqJj098jAE/DMAHCR9gdMhoDAkacsf7HvR9EEODhkKj1cDbzhudPTrjhvIGnv3jWfx5/c9a73/zhc0QBAFfDPiC493ILNR4AIOlpSWCg4MBABEREYiLi8Py5cuxevVq9O/fH8nJycjOzoZCoYCTkxO8vLwQFBR01225ublBLpffcSVRZmYmvLzuPc28lZVVla4nqjlHK0cEOgTiXM45zrlAZGa2JW/DwkMLYSm3xJCgIRjfZjz8HO7ekmwht8DinourLNOJOszaNwuz98/Gl82+RLh7eI1rOJF1Au3d2vP+QWQ26tz0odPpoFKpqixzc3ODk5MTdu/ejaysLDzyyN1PiJaWloiIiMCuXbuqbG/Xrl2Iioqqa2lUjXD3cOy8vhNlmjKpSyGi2/LK8vBe3HuI9o3Grsd24Y3ub9wzrNyLTJBhUfQihLqEYua+mSjVlNZo/ezSbJzOPo3+gf1rtB6RKdUosMTGxmLfvn24du0aEhMTERsbiz179mDs2LEAgDVr1uDIkSNITk7Ghg0b8Nhjj2HatGkICQnRb6Nv3776K4IAYPr06fjss8+wbt06nDt3DpMmTUJxcTEmTJhgpEOke3ku/Dlkl2bj/fj3Od8CkRnQ6DR4de+rECAgNjIWzSya1XpbVnIrvB39tv7qwPvJLcvF6Vun9fM0NVM0g72FPbYlb0NReVGtayAyphp1CWVlZeGpp55Ceno6HB0dER4ejh07dqBfv34AgAsXLiA2Nha5ubkIDAzEnDlzMG3atCrbqOwyqjR69GjcunUL8+bNQ0ZGBjp27Ijt27ffMRCXjC/AIQCvR76Ot468hTJNGeZ0nwMrObvaiKSy8dxGJGQm4PP+nxvl7usBDgGY3mU6lh5bii6eXRATcOf0Et+c/wbvHnsXGlEDC5kFJnWYhGfbP4v3e7+PaX9Nw/jt4/Fxn4/hY+dT53qI6oJT8xN+uvwT3jryFiK9I/HhQx/CQs47shLVN41Og8d+fgwtnVpiWa9lRtuuKIp4fufzSCtOwy8jfqny2pZLWzDv0DyMajUKo0NGY/u17fgy6UtEeUfhufDnYGdph8d+fgytnFvhx0d+NFpNRJVqcv7mrGGEYcHD4Gbjhim7p2DSrkn4NOZT3kaeqB6JoojFRxfjWsE1vNH9DaNu+3jmcSTlJCHAPuCO17698C16Ne+F+VHzIQgCwlzDEOEZgffi3sOEHX93y0d6RRq1JqLaYGAhAEAP3x5YHbMaz+98Hu8ffx+zus2SuiSiJkEURXyR9AU2X9yMNx94ExGeEUbb9ofxH+KLpC8Q6RWJ/z703zteL1YXo5NHpyqXLT/Y/EH08OmBszlncTHvItybuaOHTw+j1URUWwwspNfNuxte6/oalhxbAi9bL4xvO17qkogaNVEUMXTrUFxXXsez7Z/FiFYjjLbtI+lH8EXSFwCAlTErYSm3rPL6vtR9uKa8hsmdJt+xrlwmR3v39mjv3t5o9RDVFQMLVfFk2JNIL07Hf4//FwMDB8LTloOfiUzluvI6riuvY3LHyXihwwtG3fa1gmsAgBldZlS5Z1hGcQY2ntuIjec2olfzXugfwEuXqWFgYKE7jAkdg7Vn1uJi3kUGFiITKtYUAwDauLYx+rZHtRqFG4U3sOz4Mvx+9Xf0D+yPi3kXsePqDtgobDCh3QQ8F/4cZIJxZyInMhUGFrqDVtQCAK8WIjKxNi5tEOwUjO1Xt+PB5g8addsWcgvM7DoTvZr3wvqz67Hq1Cq4WLvg1S6vYkSrEbC1sDXq/ohMjYGF7qDWqgEAljLLat5JRHUhCAKcrZ1rPBNtTUR6RyLSm1f5UMPHtkC6Q+UVAyIa/BQ9RGZNrVUjKTuJg1uJDMDAQneobCouUBVIXAlR46XRafDmkTdRpilDr+a9pC6HyOwxsNAd3G3c4WrtioM3D0pdClGjU1RehF+u/IKnfn8KvyT/grej30ZLp5ZSl0Vk9jiGhe4gCAKeavsUPkr4CBGeERjUYlCViaWIqOYSMhPw9tG3cTn/MnSiDh3cO2DNwDXo6NFR6tKIGgQGFrqr/7T5Dy7kXsCs/bOw8fxGhLuFw9/BHwH2AWjl3ApuNm4MMUQGyC3LxbK4Zfj5ys9o79Ye87rPwwM+D8Dbzlvq0ogaFN78kO5JFEXsv7kf3134DteV15FalAqNTgMAcLZyRmvn1mjt0hqRXhVXIVgrrGu8j9yyXFwtuIpybTkcLB3QwrEFmlk0M/ahEElCq9NizK9jkF6cjlc6v4LhwcMhl8mlLovIbNTk/M3AQgbT6rRIK0rDxfyLuJh7ERfzLuJc7jncLLoJALCQWWBO5Jx7/vUoiiJSi1JxIusEEm8l4tStUziXe67KexQyBR5p+QjGtxmPIKegejkuIlOJz4zH09ufxpcDvkRXr65Sl0Nkdni3ZjIJuUwOPwc/+Dn4oa9/XwAVIeSq8io2nN2AzRc3Y8HhBQAADxsPOFk7wc7CDgqZAoIg4HLeZeSU5QAAAh0C0d6tPca1GYd2ru1gpbBCflk+jmYcxccJH+PHSz/CxdoFY0LHIMAhAP72/vB38Ie9pb1Uh09UYzYKGwDApbxL6OzRma0rRHXAFhYyqvyyfMRnxeNM9hkUlheiSF0EjU4DnahDgEMAOrh3QEePjnC0crznNorKi/DKnleQU5qDnNIc5Kny9K/52fshxj8GY8PG8rYBZqCy1exS3iUoZApEekfCSm4ldVlmQxRFzNo/C79f/R2hLqFY1msZAhwCpC6LyGywS4gaFWW5EinKFFxXXkdcZhx2Xt8JURTxxYAvEOoSKnV5TU5heSF2XNuBgzcPIj4zvkqgdLZyxtqBa9md9y8ns07i9QOvw8HSAd8M+YYD1oluY2ChRq1AVYBn/3gWVnIrbBi8QepymowLuRew6cIm/HrlV6i0KnR074guXl3Qwb0DQl1CkZyfjMVHF8NCboEfH/lR6nLNzs/JP+P1A6/ji/5foJt3N6nLITILHMNCjZqjlSP+0+Y/mHNgDnJKc+Bq4yp1SY2CVqfFhnMbcDj9MLJKsmCjsIGvrS88bT2RmJ2I+Mx4eNh44Jl2z2Bkq5HwaOZRZX2PZh54seOLmLlvJvLK8uBs7SzRkZgnta7iHl0T/5iIxPGJEldD1PBwpltqkHr49IBCpsCCwwuQWpgqdTmNwjtx7+CD+A8gF+To6N4RgQ6BuFV6C7tu7IJMkGFZr2XY/uh2vNDhhTvCSqV8VT4UMgUvTb+LQS0GSV0CUYPGFhZqkFxtXPHug+9i0ZFFGLJlCFo4tECISwi6enVFv4B+9x3UawwqrQo3lDeQVpQGC5kF/Bz80NyueYMdm7AvdR++Of8NZnebjbFhY2u9naTsJIQ4h3Dg7V3YKGwwu9tsLD22lC1QRLXAwEINVr+Afoj2jcaOazuQlJ2EcznnsP3adqw4sQIbBm9Ac/vmRttXXlkeTmadREJWAo5nHMe53HPQitoq7wl2CsaY0DEY2WokFLKG8aNVpinDtuRteOfYO3jI7yGMCR1Tp+2dyDqBaN9oI1XX+OhEHRSCAjKBjdtENdUwfqsS3YONwgbDg4djePBwAEBGcQbG/DoG686sw5zuc2q93VO3TmH71e3IU+XhTPYZXFNeA1AxTqOLZxeMaDUCwU7B8LXzhVbU4kLuBWxL3oZFRxbhx0s/4r1e78HP3s8IR2h8Gp0GF3IvYMf1Hfjx0o8oUBXgkZaPYH7U/DqdSEVRREZxxj27i5o6URTx7YVv66UFkKgxYmChRsXL1gv9Avphb8pevC6+XuMumlJNKWbvm43dKbvhZesFZytndPXqiuc7PI9OHp3gY+tz12362Pmgt39vJN5KxKz9s/DotkfRw7cHmts1R3P75vCz90Nz++bwtvWWrPUlrSgNi48uRlxGHEo0JbC3tMeI4BEYHTIa/g7+dd6+IAjo498Hq0+t1l9BRH8TISK1MBWPtnpU6lKIGiRe1kyNzsGbB/HCny/gu4e/Q5hrWI3W/fjEx1h3Zh0W9ViE/oH9a9XioCxXYsPZDYjLiEN6cToyijP03UcKQQFvO2/42fvBz94PIS4h6OTeCS2dWpp0/MvlvMt4budzsJRb4tHWjyLCMwJtXdvCUm5p1P2Uacrw/M7nkVWShZ+G/2T07Td0L/75IgrLC7F+8HqpSyEyC7ysmZq0bl7d4GXrhZn7ZmJwi8EIdg6Gm40bXK1d4W3rDQu5xT3X3ZOyBwMCB2Bgi4G13r+DpQNe7Pii/rlap0ZGUQZSClOQUpiC1KJUpBSmID4zHt9f/B5aUYv2bu3xTLtn8JDfQyZpgXk//n1YK6zx1aCv4GbjZvTtV7JWWGNO9zkYtW0UDqcdRi+/XibbV0MU7RuN9+Lew82im/C185W6HKIGhYGFGh0LuQVW9FmBj098jI3nN6JAVaB/zc7CDo+HPI5o3+g77u2SU5qDS3mX8J82/zFuPbevIvJzuHNMS6mmFEfSjmD9ufWYtmcaghyDMC1iGh7ye8ho+79ZdBOH0g4htlusScNKpVZOrQAAu1N2M7D8yyMtH8GXSV9i5cmVeDv6banLIWpQGFioUQpxCcGKvisgiiIKVAXIKctBdmk2/rz+J76/+D2+TPoSLtYu6OPfB339+8JSZol1Z9fB1sIWDzZ/sN7qtFHYoLd/b/34l+UnlmPq7qlY1GMRhgUPu+P9oijibM5ZpBSmIMw1rNr70hSoCjDtr2lwb+aOh1s+bKrDqKKya0utVdfL/hoSO0s7DG05FD9c/AE6UcerhYhqgIGFGjVBEOBk7QQnaye0dGqJSO9IxEbGIjE7EX9e/xM7r+/E9xe/B1BxH5wFDyyAi7WLJLW2d2+Pz/p9hsm7JuOHSz/cEVi2X9uOjxM+xo3CG/plLRxboLdfb3T16gqNToPM4kwUqYsgE2QoLC/E9xe/h0anwZqBa2BrYVtvx9LKuZX+TsVUVbRvND5P/Bzncs6hrVtbqcshajAYWKjJkQkydHDvgA7uHTA9YjquFlyFCBGBDoFVuoikIAgCejXvhbePvo3k/GS0dGoJURTx4q4XceDmAfT07Ym5UXMR6hyKhKwE/JXyF3689CO+TPoSQMWg3mYWzSCKImQyGfoF9MOLHV6EezP3ej2OYMdgnMk5U6/7bCh0og4AJP+/RtTQ8CohIjOTV5aHZ3Y8g4ziDIxvOx6nb53G/pv7AQAJ4xLuGDSs0WmQVZIFC5kFXG1czaKbYdf1XXhlzyvYNGRTk2lFUGlV2J+6H9eU17D+7HqMCB6B4cHD4e/gr/9MLuddxptH3kRKYQp2PbbLLD4rIinxbs1EDVxReRGWHFuCXTd2wdbCFnMi56C3X+8GM/W/VqfF8J+Gw9bCFiv6rqiXwb5SupR3CVN3T8XNoptwsHSAslypf81KbgV7S3sUlRehTFsGb1tvvPPgO+jk0UnCionMAwMLUSMhimKDCSn/dib7DCb9OQl5qjyMDhmNmV1nNsp5Wb468xXeO/4egp2C8d6D7yHYORiiKCKnLAcXcy8iuSAZReoi2FvYw8fOBz19e9730nqipoSBhYjMQm5ZLkb/Mlo/Zf+EthMwJGhIo7rxX/t17QEA8ePiG2UgIzIlThxHRGbBxdoFOx/diSsFV/BF4hdYdnwZ3o17F9623rCUW0IQBAgQ9GM5HK0cMTZsLPr6920w4zsCHQLR2bMzwwqRiTGwEJHJBTkG4e3ot/FK51dwMO0gbihvQK1TQxRF6KCr+CrqcDHvIqbvmY5Ah0BM6jAJg1oMMvsusWJ1MVytXaUug6jRq1Fg+fTTT/Hpp5/i2rVrAIC2bdti3rx5GDRoEAAgIyMDr732Gnbu3InCwkKEhIRgzpw5GDVq1D23uWDBAixcuLDKspCQEJw/f76Gh0JE5s69mbv+ztp3I4oiTt06hS8Sv8Cs/bPw46UfsTJmpdm2XmQUZ+BW6a0mcyUUkZRq1ObavHlzLF26FPHx8Th+/Dj69OmDYcOG4cyZivkWnnrqKVy4cAHbtm1DYmIiRo4ciccffxwnTpy473bbtm2L9PR0/ePAgQO1PyIiarAEQUBHj474uO/HWBWzCglZCVh2fJnUZd3TT5d/gkKmQEf3jlKXQtTo1SiwDB06FIMHD0arVq3QunVrvP3227Czs8ORI0cAAIcOHcLUqVPRrVs3BAUF4Y033oCTkxPi4+Pvu12FQgEvLy/9w82tcV8CSUTV6+HbA7O6zsI357/B6lOrpS7nrnZc34HBLQbD1YZdQkSmVutRbVqtFps2bUJxcTGioqIAAA888AC+/fZb5ObmQqfTYdOmTSgrK8NDDz10321dunQJPj4+CAoKwtixY3Hjxo37vl+lUkGpVFZ5EFHjMzp0NKZ0nIIVJ1dg5cmVMLeLGgtUBfCx85G6DKImocaDbhMTExEVFYWysjLY2dlhy5YtaNOmDQDgu+++w+jRo+Hq6gqFQoFmzZphy5YtCA4Ovuf2IiMjsXbtWoSEhCA9PR0LFy5Ez549kZSUBHt7+7uus2TJkjvGvRBR4/R8h+chl8mxPGE5LudfxowuM8wiJGh0GhSri2EpM8/xNUSNTY3nYSkvL8eNGzdQUFCA77//Hp9//jn27t2LNm3aYOrUqTh27BgWL14MNzc3bN26FR988AH279+P9u3bG7T9/Px8BAQE4P3338fEiRPv+h6VSgWVSqV/rlQq4efnx3lYiBqx7de2Y8nRJcgry0MP3x54tPWjeKj5Q5Ldk2fT+U1YfHQxNg/djBCXEElqIGro6nXiuJiYGLRs2RIzZ85EcHAwkpKS0LZt2yqvBwcHY9WqVQZvs2vXroiJicGSJUsMej8njiNqGkrUJdh+bTu+v/g9ErMT0dKxJVb1WwUvW696r+WZHc+gmaIZVvRdUe/7JmosanL+rvPMTDqdDiqVCiUlJRUblFXdpFwuh06nM3h7RUVFSE5Ohre3d11LI6JGpplFM4xsNRJfD/kaXw/+GkXqIrx15K16ryOjOAMnMk8g2je63vdN1FTVKLDExsZi3759uHbtGhITExEbG4s9e/Zg7NixCA0NRXBwMJ5//nkcO3YMycnJ+O9//4udO3di+PDh+m307dsXK1b8/RfJjBkzsHfvXly7dg2HDh3CiBEjIJfLMWbMGKMdJBE1Pu3d2+O58Odw8OZBFJYX1tt+S9QlmHdwHpysnTA4aHC97ZeoqavRoNusrCw89dRTSE9Ph6OjI8LDw7Fjxw7069cPAPDbb79h9uzZGDp0KIqKihAcHIx169Zh8OC/f6iTk5ORnZ2tf56amooxY8YgJycH7u7uiI6OxpEjR+Du7m6kQySixqqLVxdoRS2SspMQ5RNVp22Joojk/GQczTiKnNIcdPToCLVWjezSbGSXZSO7NBvXlddxNucs1Fo1PurzERws2QVNVF9480MiarB0og49N/XEqFajML3L9Bqtq9KqcDn/Ms7nnEdcZhyOph9Fdmk2LGQWsLe0R25ZLgBALsjhau0Kt2Zu8LXzRZhLGAYHDYavna8pDomoSeHND4moSZAJMowIHoGvz3+NMaFj4G1377FvKq0Kp7JO4Uj6ERxNP4qzOWehETUQICDMNQxDWw5Fd6/u6OTZCdZya6QWpcLWwhZOVk4N5kaMRI0ZW1iIqEHLLM7E4788DgAYGjQUgY6B8LHzgSiKSCtOQ2phKhKzE3Eq6xTKdeVwtnJGpHckunh2QZhrGIKdgtHMopnER0HUNNXrZc3mgIGFqGnLKM7AqlOrcDjtMDJKMqATK65MlAkyeDXzQmuX1ujm1Q3dvLqhlXMrtpgQmQkGFiJqstRaNTJLMgEAnraesJBZSFwREd0Lx7AQUZNlIbdAc/vmUpdBREbGdlEiIiIyewwsREREZPYYWIiIiMjsMbAQERGR2WNgISIiIrPHwEJERERmj4GFiIiIzB4DCxEREZk9BhYiIiIyewwsREREZPYYWIiIiMjsMbAQERGR2WNgISIiIrPXKO7WLIoigIrbVBMREVHDUHnerjyP30+jCCyFhYUAAD8/P4krISIiopoqLCyEo6Pjfd8jiIbEGjOn0+mQlpYGe3t7CIIApVIJPz8/pKSkwMHBQery6C74GZk/fkbmj5+ReePnUz1RFFFYWAgfHx/IZPcfpdIoWlhkMhmaN29+x3IHBwf+JzFz/IzMHz8j88fPyLzx87m/6lpWKnHQLREREZk9BhYiIiIye40ysFhZWWH+/PmwsrKSuhS6B35G5o+fkfnjZ2Te+PkYV6MYdEtERESNW6NsYSEiIqLGhYGFiIiIzB4DCxEREZk9BhYiIiIye40usFy8eBHDhg2Dm5sbHBwcEB0djb/++qvKewRBuOOxadMmiSpuegz5jCrl5OSgefPmEAQB+fn59VtoE1bdZ5STk4OBAwfCx8cHVlZW8PPzw5QpU3g/r3pS3edz6tQpjBkzBn5+frCxsUFYWBiWL18uYcVNjyG/51566SVERETAysoKHTt2lKbQBqTRBZaHH34YGo0Gu3fvRnx8PDp06ICHH34YGRkZVd63Zs0apKen6x/Dhw+XpuAmyNDPCAAmTpyI8PBwCaps2qr7jGQyGYYNG4Zt27bh4sWLWLt2Lf7880+88MILElfeNFT3+cTHx8PDwwMbNmzAmTNnMGfOHMTGxmLFihUSV950GPp77plnnsHo0aMlqrKBERuRW7duiQDEffv26ZcplUoRgLhz5079MgDili1bJKiQDP2MRFEUV65cKfbq1UvctWuXCEDMy8ur52qbppp8Rv+0fPlysXnz5vVRYpNW28/nxRdfFHv37l0fJTZ5Nf2M5s+fL3bo0KEeK2yYGlULi6urK0JCQvDVV1+huLgYGo0Gq1evhoeHByIiIqq8d/LkyXBzc0O3bt3w5ZdfGnRra6o7Qz+js2fP4s0338RXX31V7Q2xyLhq8nNUKS0tDT/++CN69epVz9U2PbX5fACgoKAALi4u9Vhp01Xbz4iqIXViMraUlBQxIiJCFARBlMvlore3t5iQkFDlPW+++aZ44MABMSEhQVy6dKloZWUlLl++XKKKm57qPqOysjIxPDxcXL9+vSiKovjXX3+xhaWeGfJzJIqi+MQTT4g2NjYiAHHo0KFiaWmpBNU2PYZ+PpUOHjwoKhQKcceOHfVYZdNWk8+ILSyGaRB/us6ePfuuA2X/+Th//jxEUcTkyZPh4eGB/fv349ixYxg+fDiGDh2K9PR0/fbmzp2LHj16oFOnTpg1axZmzpyJ9957T8IjbPiM+RnFxsYiLCwM48aNk/ioGhdj/xwBwAcffICEhAT89NNPSE5OxvTp0yU6uobPFJ8PACQlJWHYsGGYP38++vfvL8GRNR6m+ozIMA1iav5bt24hJyfnvu8JCgrC/v370b9/f+Tl5VW5lXerVq0wceJEzJ49+67r/vrrr3j44YdRVlbGez7UkjE/o44dOyIxMRGCIAAARFGETqeDXC7HnDlzsHDhQpMeS2Nl6p+jAwcOoGfPnkhLS4O3t7dRa28KTPH5nD17Fr1798azzz6Lt99+22S1NxWm+hlasGABtm7dipMnT5qi7EZDIXUBhnB3d4e7u3u17yspKQGAO8Y8yGQy6HS6e6538uRJODs7M6zUgTE/ox9++AGlpaX61+Li4vDMM89g//79aNmypRGrblpM/XNU+ZpKpapDlU2XsT+fM2fOoE+fPhg/fjzDipGY+meI7q9BBBZDRUVFwdnZGePHj8e8efNgY2ODzz77DFevXsWQIUMAAD///DMyMzPRvXt3WFtbY+fOnVi8eDFmzJghcfVNgyGf0b9DSXZ2NgAgLCwMTk5O9V1yk2PIZ/Tbb78hMzMTXbt2hZ2dHc6cOYPXXnsNPXr0QGBgoLQH0MgZ8vkkJSWhT58+GDBgAKZPn66/lFYulxt0wqW6MeQzAoDLly+jqKgIGRkZKC0t1bewtGnTBpaWlhJVb8akGz5jGnFxcWL//v1FFxcX0d7eXuzevbv422+/6V///fffxY4dO4p2dnaira2t2KFDB3HVqlWiVquVsOqmpbrP6N846Lb+VfcZ7d69W4yKihIdHR1Fa2trsVWrVuKsWbP4GdWT6j6f+fPniwDueAQEBEhXdBNjyO+5Xr163fVzunr1qjRFm7kGMYaFiIiImrYGcZUQERERNW0MLERERGT2GFiIiIjI7DGwEBERkdljYCEiIiKzx8BCREREZo+BhYiIiMweAwsRERGZPQYWIiIiMnsMLERERGT2GFiIiIjI7DGwEBERkdn7fwHn3780iUsgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 7 small or perfect counties that we would keep whole in county-snapped Home Districts\n"
     ]
    }
   ],
   "source": [
    "#quick block to flag the whole county-districts\n",
    "MAP = dummyPoly\n",
    "perfectCounty = [0]*nCounties  #\"perfect\" counties have the perfect population for a whole district\n",
    "wholeCounty = [0]*nCounties    #\"whole\" counties are either perfect or small enough that they should be kept whole\n",
    "minCountyRatio = 0.15   #small counties will naturally be unsplit.  We'll set a reasonable arbitrary threshold.\n",
    "lockedTract = [0]*nTracts\n",
    "nDistricts = 99 #Ohio legisl\n",
    "statePop = np.sum(tractPop)\n",
    "avgDistrictPop = statePop/nDistricts\n",
    "aDP = avgDistrictPop\n",
    "maxDevn = 0.05\n",
    "print(nDistricts,\"districts, each with avgDistrictPop of\",r3(avgDistrictPop))\n",
    "print(\"I will now build a map from the counties and display the whole county districts\")\n",
    "for c in range(nCounties):\n",
    "    if MAP == dummyPoly:\n",
    "        MAP = countyGeom[c]\n",
    "    else:\n",
    "        MAP = MAP.union(countyGeom[c])\n",
    "    if countyPop[c] >= (1.-maxDevn)*avgDistrictPop and countyPop[c] <= (1.+maxDevn)*avgDistrictPop :\n",
    "        perfectCounty[c] = 1\n",
    "        wholeCounty[c] = 1\n",
    "        plotPoly(countyGeom[c])\n",
    "    if countyPop[c] < minCountyRatio*avgDistrictPop:  #small counties will naturally be unsplit.\n",
    "        wholeCounty[c] = 1\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "print(\"there are a total of\",np.sum(wholeCounty),\"small or perfect counties that we would keep whole in county-snapped Home Districts\")\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract == 0:\n",
    "        if wholeCounty[countyNo[t]] == 1:  #this tract is part of a whole county per Article XI.3.c.2\n",
    "            lockedTract[t] = 1             # ... or in a county that is so small we are keeping that county whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "53043518-66b4-4740-a4cb-d3109131b322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "below is copied from OH_615COIsnap15, but with alternate COI file\n",
      "there were 45 blockgroups with tractPop < 10\n",
      "I read in GEOID and clusterID for each of 9435 blockgroups. We expected 9427\n",
      "...assuming bg's w pop < 10 were not SKATER-assigned.  But there are a total of 9466 nonskipped bgs\n",
      "after merging, we are left with 9435 active blockgroups\n",
      "working on adding bg 0 to a COI cluster\n",
      "working on adding bg 2000 to a COI cluster\n",
      "working on adding bg 4000 to a COI cluster\n",
      "working on adding bg 6000 to a COI cluster\n",
      "working on adding bg 8000 to a COI cluster\n",
      "I formed 1850 COI shapes.  Now I plot them.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now check if any skipped tracts are in the BG assignment list\n",
      "tract 6080 with low pop 0 was assigned to COI -999 and county -999\n",
      "tract 3774 with low pop 0 was assigned to COI -999 and county -999\n",
      "tract 9049 with low pop 0 was assigned to COI -999 and county -999\n",
      "tract 2675 with low pop 0 was assigned to COI -999 and county -999\n",
      "tract 6268 with low pop 0 was assigned to COI -999 and county -999\n",
      "tract 8741 with low pop 0 was assigned to COI -999 and county -999\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"below is copied from OH_615COIsnap15, but with alternate COI file\")\n",
    "nLowPopTracts, minTractPop = 0,10\n",
    "for t in range(nTracts):\n",
    "    if tractPop[t] < minTractPop:\n",
    "        nLowPopTracts += 1\n",
    "print(\"there were\", nLowPopTracts,\"blockgroups with tractPop <\",minTractPop)\n",
    "#READ IN blockgroup COI assignments (FROM Katy)\n",
    "COIfile = pd.read_csv(\"state_map_files/OH_ClustersbyBG_Mun16Dec.csv\")  #or ..._SC.csv\n",
    "COI_GEOID = COIfile['GEOID']\n",
    "COIgeoid = [str(COI_GEOID[i]) for i in range(len(COI_GEOID)) ]  #pd had interpreted geoid's as nos\n",
    "clusterID = COIfile['CLUSTER_ID']  #Katy numbers these 1-1850 or 1 - 615\n",
    "clusterID = clusterID.to_numpy()\n",
    "print(\"I read in GEOID and clusterID for each of\",len(clusterID),\"blockgroups. We expected\",nTracts-nLowPopTracts)\n",
    "print(\"...assuming bg's w pop <\",minTractPop,\"were not SKATER-assigned.  But there are a total of\",nTracts-len(skipList),\"nonskipped bgs\")\n",
    "nClusters = np.max(clusterID) - np.min(clusterID) + 1\n",
    "for i in range(len(clusterID)):\n",
    "    clusterID[i] -= 1   #renumber these starting at zero\n",
    "COIdf = pd.DataFrame( {\"GEOID20\":COIgeoid,\"clusterID\":clusterID } )\n",
    "#quick little dataframe since the COI file excludes some tracts ************************\n",
    "origTractNo = [i for i in range(nTracts)]\n",
    "miniTractPopFile = pd.DataFrame( {\"GEOID20\":GEOID20,\"origTractNo\":origTractNo} )\n",
    "mergedDF = pd.merge(miniTractPopFile,COIdf,on=\"GEOID20\")\n",
    "tractNo = mergedDF['origTractNo']\n",
    "mergedClusterID = mergedDF['clusterID']\n",
    "nActiveTracts = len(tractNo)\n",
    "print(\"after merging, we are left with\",nActiveTracts,\"active blockgroups\")\n",
    "nCOIs = nClusters  \n",
    "COIgeom = [dummyPoly]*nCOIs   #again, common language\n",
    "COIarea = [0.]*nCOIs\n",
    "muniTractList = [list()]*nCOIs\n",
    "tractMuniNo = [-999]*nTracts\n",
    "\n",
    "for s in range(nCOIs):\n",
    "    muniTractList[s] = list()\n",
    "\n",
    "for fakeT in range(nActiveTracts):\n",
    "    t = tractNo[fakeT]  #t is the true tractNo in the original tractPopFile for the mergedDF's row no (fake tract no)\n",
    "    if t%2000 == 0 :\n",
    "        print(\"working on adding bg\",t,\"to a COI cluster\")\n",
    "    s = mergedClusterID[fakeT]\n",
    "    tractMuniNo[t] = s\n",
    "    muniTractList[s].append(t)\n",
    "    if COIgeom[s] == dummyPoly:\n",
    "        COIgeom[s] = tractGeom[t]\n",
    "    else:\n",
    "        COIgeom[s] = COIgeom[s].union(tractGeom[t])\n",
    "print(\"I formed\",nCOIs,\"COI shapes.  Now I plot them.\")\n",
    "for c in range(nCOIs):\n",
    "    plotPoly(COIgeom[c])\n",
    "    COIarea[c] = COIgeom[c].area\n",
    "plt.show()\n",
    "\n",
    "print(\"I will now check if any skipped tracts are in the BG assignment list\")\n",
    "for t in skipList:\n",
    "    if t in tractNo:\n",
    "        print(\"tract\",t,\"with low pop\",tractPop[t],\"was assigned to COI\",tractMuniNo[t],\"and county\",countyNo[t])\n",
    "        plotPoly(tractGeom[t])\n",
    "        if tractMuniNo[t] >= 0:\n",
    "            plotPoly(COIgeom[tractMuniNo[t]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dd77e2a7-0a5e-4426-a703-7d092b482d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were a total of 31 low-pop non-coastal bgs not SKATER-assigned to a COI\n",
      "I will now assign them to the contiguous COI with the closest centroid\n",
      "[747, 1102, 1103, 1225, 2302, 2802, 2859, 2881, 2970, 2972, 3593, 3902, 4107, 5467, 5469, 5490, 5496, 5551, 5599, 5600, 5627, 5631, 6800, 6805, 7048, 8077, 8789, 8879, 9293, 9312, 9375]\n"
     ]
    }
   ],
   "source": [
    "missedBGlist = list()\n",
    "for b in range(nTracts):\n",
    "    if b not in skipList and tractMuniNo[b] < 0:\n",
    "        missedBGlist.append(b)\n",
    "print(\"There were a total of\",len(missedBGlist),\"low-pop non-coastal bgs not SKATER-assigned to a COI\")\n",
    "print(\"I will now assign them to the contiguous COI with the closest centroid\")\n",
    "for b in missedBGlist:\n",
    "    closeCOIs = list()\n",
    "    for c in range(nCOIs):\n",
    "        if isRookAdj(tractGeom[b],COIgeom[c]):\n",
    "            closeCOIs.append(c)\n",
    "    closeDists = [tractGeom[b].distance(COIgeom[c].centroid) for c in closeCOIs]\n",
    "    cc = closeCOIs[closeDists.index(np.min(closeDists))]\n",
    "    tractMuniNo[b] = cc\n",
    "    muniTractList[cc].append(b)\n",
    "    COIgeom[cc] = COIgeom[cc].union(tractGeom[b])\n",
    "\n",
    "for b in range(nTracts):\n",
    "    if tractMuniNo[b] < 0 and b not in skipList:\n",
    "        plotPoly(tractGeom[b])\n",
    "        print(\"unassigned noncoastal blockgroup\",b,countyNo[b],tractPop[b])\n",
    "print(missedBGlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0c20199f-a1d1-4c15-b71e-da0c7702aba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing the counties where we assigned BGs missing from original COI list\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tk5WRkaFbb71Vt99+e7vbZ2dna/78+ZozZ05g2QMPPKBnnnlGH374YYfvm56ergceeEA/+tGP2n29vZ6a7Oxs1dXVBcb8hNoe9x5lOwlPAID+Ybd7t3KcOWF9D7fbLZfL1anP76BPP32d3+9vEx4kacCAAZKk8vJyVVdXa8qUKR1u39DQcEJPjtVqPekVU3v37tWhQ4eUmZnZ4ToOh6PN+B4AABBavW0m/aBCTUlJiYqKipSTk6P6+notX75ca9asUVlZmSRp6dKlGjlypNLT07V+/XrdfPPNKi4uVl5eXqCNcePGadq0aZo7d66k1iuqFixYoJycHJ1xxhnasmWLfvWrX+n666+XJB05ckT33nuvrr76amVkZKiiokJ33HGHhg8frokTJ4bq5wAAAPq4oEJNdXW1Zs6cqcrKSrlcLuXn56usrEwTJkyQJO3cuVMlJSU6fPiwhgwZorvuukvFxcVt2qioqNDBgwcDzx9//HHdfffd+vGPf6zq6mplZWXpRz/6ke655x5Jrb0277//vpYtW6ba2lplZWWpsLBQ999/Pz0xAABEUG+bSb/bY2r6imDOyXUVY2oAAP1JbxtTw72fAABAl/S2MTWEGgAAEDS/6Zell8WI3lUNAADoEw4dPaS0uLRIl9EGoQYAAATtaMtRxdvjI11GG4QaAAB6qdrGWtU31cvn90W6lBP0tvE0Uggm3wMAAOFx8OhBpcSmqNZbe9I7YpsyT3l5td1iV0JMghJsCbJa2r9vYl9HqAEAoJdyWB1Ki0tTmro/dqXJ1yRPs0f7PftPCEiGjEDPy/HhqN2wZEhxtjg1+5q7XVOoEWoAAOiFmnxNslvtIWsvxhqjGGuMUmJTutWO3/TraMtRxSTEhKiy0CHUhJhpmr3qjqUAgL7p4NGDSo9Pj3QZJ7AYFiXYEyJdRrsYKBxCdqtdLf6WSJcBAIgCLf4W2S2h66npDwg1IWSz2NTs733nGAEAfU9vu69SX0CoCSG7xU6oAQAgQgg1IUSoAQAgcgg1IWS3MKYGABAavXFyu96OUBNCNoutV163DwBAf0CoCSEu5QYAhAoDhYNHqAEAAFGBUAMAQC/jafYozh4X6TL6HEINAAC9zOHGw0qNTY10GX0OoQYAgF7GNE1ZDD6ig8VPDACAXoZBwl1DqAkx5hUAACAyCDUAACAqEGpCjC5DAEB30evfNYQaAAAQFQg1IUa6BgB0F73+XUOoAQCgFzFNfjnuKkJNiJGuAQDd4W5yy+lwRrqMPolQAwBAL1LnrZMzhlDTFYQaAAB6GcOg178rCDUAACAqEGoAAOhFGJvZdYQaAAAQFQg1AAD0Isx31nWEGgAAEBUINQAA9CKMqek6Qg0AAIgKhBoAAHqJZl+zrBZrpMvos4IKNaWlpcrPz5fT6ZTT6VRBQYFWrVoVeL2iokLTpk1Tenq6nE6nZsyYoaqqqpO26fP5dPfddys3N1dxcXEaNmyY7r///jb3vjBNU/fcc48yMzMVFxen8ePH6+OPPw5yVwEA6N1qvDVKdiRHuow+K6hQM3jwYC1evFibN2/Wpk2bdPnll2vq1Knavn27PB6PCgsLZRiGysvLtW7dOjU1NWny5Mny+/0dtvnggw+qtLRUv/nNb/TBBx/owQcf1EMPPaTHH388sM5DDz2kxx57TE888YQ2bNighIQETZw4UY2NjV3fcwAAepnGlkbF2+MjXUafZZjdvB1oamqqHn74YWVnZ6uoqEg1NTVyOlvvWVFXV6eUlBS99tprGj9+fLvbX3nllRo0aJB+//vfB5ZdffXViouL0zPPPCPTNJWVlaVbb71Vt912W6DdQYMG6amnntI111zTqTrdbrdcLpfq6uoC9YXDHvceZTuzw9Y+ACB67XbvVo4zJ9Jl9CrBfH53eUyNz+fTc889J4/Ho4KCAnm9XhmGIYfDEVgnNjZWFotFa9eu7bCdsWPHavXq1froo48kSe+9957Wrl2roqIiSdKuXbt04MCBNqHI5XLpggsu0Pr16zts1+v1yu12t3kAAIDoZQt2g23btqmgoECNjY1KTEzUihUrNGrUKKWnpyshIUHz5s3TwoULZZqm5s+fL5/Pp8rKyg7bmz9/vtxut04//XRZrVb5fD4tWLBA1157rSTpwIEDkqRBgwa12W7QoEGB19qzaNEi3XvvvcHuHgAA6KOC7qnJy8vT1q1btWHDBs2ePVuzZs3Sjh07lJ6erhdeeEErV65UYmKiXC6Xamtrde6558pi6fhtnn/+eT377LNavny53n33XS1btky/+MUvtGzZsm7tWElJierq6gKPPXv2dKs9AADQuwXdUxMTE6Phw4dLksaMGaONGzfq0Ucf1ZIlS1RYWKiKigodPHhQNptNycnJysjI0NChQzts7/bbb9f8+fMDY2NGjx6tzz//XIsWLdKsWbOUkZEhSaqqqlJmZmZgu6qqKp199tkdtutwONqcCgMAANGt2/PU+P1+eb3eNssGDBig5ORklZeXq7q6WlOmTOlw+4aGhhN6cqxWa+CKqdzcXGVkZGj16tWB191utzZs2KCCgoLulg8AQK/RqdmE/T6pvkqqfF/64qPwF9WHBNVTU1JSoqKiIuXk5Ki+vl7Lly/XmjVrVFZWJklaunSpRo4cqfT0dK1fv14333yziouLlZeXF2hj3LhxmjZtmubOnStJmjx5shYsWKCcnBydccYZ2rJli371q1/p+uuvlyQZhqFbbrlFDzzwgEaMGKHc3FzdfffdysrK0lVXXRWiH0PocCMyAEBXtfkMqdsnvfdHad9mqemI1OSRmhqkur1SU/1X67mypfQ8aUCeNGDEV98npPX8DkRYUKGmurpaM2fOVGVlpVwul/Lz81VWVqYJEyZIknbu3KmSkhIdPnxYQ4YM0V133aXi4uI2bRw7PXXM448/rrvvvls//vGPVV1draysLP3oRz/SPffcE1jnjjvukMfj0Y033qja2lpdfPHFevXVVxUbG9udfQ8L7tkBAOiSms9llP+39HG5lHW29PnbksUm5RRICelSyhDJniAlDmwNLokZUu1nUuV70sGPpY9elTaUSuaXc8MlDJT+ZcyXj3NbH3Epkdu/HtDteWr6CuapAQD0Rrt2bNSBt5erYO//pz02m7JbWqQRhdKQi6Ux10mxrs431uKVDlVIBz+Sqv7Z2suzb7PUWNf6euqwr4LO4POkQWdK9t7XQXC8YD6/gx4oDAAAuufznVu1f92zyty7Srn+PUpTvNb6ztCrtjP1wH8/2fWGbQ5p0KjWxxlXtS4zTenwp63hZu+m1q87/iz5miSLXTrrGmnqb0KxWxFHqAEAoAfs/eSf2rP2GWXsXqVc/2dKM+P0QfK/qTb/Zxp50RSVvfqpNuzZGfo3NgwpbVjrI39G67KWJqlqm/Ti9dL2P0tDL5VGfyf0793DCDUAAISJ6ffr3VeXyrX5fzTcV6FU06EdzotVO/p2jfy3aTo/LiGw7uGGBrli43qmMFtM6ymoqf8j/Xm2tP5/CDVon2maMgwGDANAf/bhhtdkvP4zjWnZqfdiz9e75/9Uoy75js6LT2x3/UNHa5UWl9yzRQ65WBo0Wvp8nVR2lzRxQc++f4h1e54atGWz2NRitkS6DABANy1atEjnn3++kpKSNHDgQF111VXaubPt6aEf/ehHGjZsmOLi4pSenq6pU6fqzVf/rC0PX6HTV02X1WzR66Mf0Z1/d+jib/9YOUOG6vbbb1dLy1efEy+99JImTJigl26ZrqduLFRBQUFgqpQecc73JYdTev//eu49w4SemhCzW+1q9jXLbrFHuhQAQDe89dZbmjNnjs4//3y1tLTozjvvVGFhoXbs2KGEhNbTRmPGjNG1116rnJwc7f78U9019zrN+u5Krb85V5vOW6yzJv1Q08aMUUZGht5++21VVlZq5syZstvtWrhwoSTpb3/7myZMmKAvzrhM4844R45df9fkyZO1YcMGnXPOOV0rfs8/pJdukJIype+/JMXEt79ew2Hpg5VS3e7WK636OC7pDrE6b+tlcy5HEJfgAQB6vS+++EIDBw7UW2+9pUsuuSSw3PT7tfmVJzXk3UWqOFCvC5fUaPs/39eoM0Zr1apVuvLKK7V///7AjZmfeOIJzZs3T1988YViYmIC7Zy7aLn+4xvn6yfjRuiMM87Qd7/73TZztgVl3WPS63e3fj/qKumin7aOoTnerr9JL/6w9TLwiQtae2x64dAJLumOILvFroaWhkiXAQAIsbq61l9aU1NTA8s+37lVR/70E53X9L7WxVykZS2DlJv7loaPaJ1Jf/369Ro9enQg0EjSxIkTNXv2bG3fvj3QE9Pi86v2aLMGJDnk9/tVX1/f5n2C5t7fOjnf4PNaL9/e8WdJhjTsstYJ+GJd0vvPt07IN+1JyZl5igb7BkJNiNktdrX4GVMDANHE7/frlltu0UUXXaQzzzxTTd5GbX72Ho35/Pd6eJNN55c36WjjKuXl5en1118P9MAcOHCgTaCRFHh+4MCBwLLDDU0yTSk90aFf/OIXOnLkiGbMmNH1gt37pIEjpWuelao/kLY8IzU3SJ6DkueL1hmIh10mTVsixSScur0+glATYjaLTc2+5kiXAQAIoTlz5uif//yn1q5dK0l6/42nVbB7iT63Zus/f1emGUcaVFlZqV/84heaMWOG1q1bF9StfL6ob70x9MbVf9ED996rl19+WQMHDux6we79Uvrprd8PHNnnr2rqLK5+CjEu5QaA6DJ37ly98sorevPNNzV48GBJ0shLpuv92DHK8B2Qy+XSiBEjdMkll+jFF1/Uhx9+qBUrVkiSMjIyVFVV1aa9Y88zMjICyw4eadKR7e/ovnk36/nnn9f48eO7V7R7v+TM6l4bfRChBgCAdpimqblz52rFihUqLy9Xbm5u4LWEpGQ1DDhbLbKesI1pmvJ6W3teCgoKtG3bNlVXVwfWef311+V0OjVq1KjAshUv/J8Orfy9/vD0s7riiiu6V7ivRTpyoF+GGk4/AQDQjjlz5mj58uV6+eWXlZSUFBgD43K5FOtwyNj2om7/NFM//GCn0tPTtXfvXi1evFhxcXH61re+JUkqLCzUqFGj9IMf/EAPPfSQDhw4oJ/97GeaM2eOHA6HJGn58uV6/OfFyii6Vv92UUHgfeLi4uRydeFK2iNVrXfqdv5LaH4QfQihBgCAdpSWlkqSLr300jbLly5dqvOGpSjbVqP3q9P0rW99SzU1NRo0aJAuueQSvf3224HxMFarVa+88opmz56tgoICJSQkaNasWbrvvvsC7T355JPy+1pU+coyZWYuCyyfNWuWnnrqqeALd+9v/UpPDQAAkFpPJR1v/64Ptfvt/1Pi7qU6fdf72pl2ltasLZfNHtNBC61OO+00/fWvf+3w9TVr1mjib1borEG5emj62d0v3L2v9SuhBgAAHM/0+7Xhybk6r/KPSpVVH8Wdrc2j5uucq24+ZaDpVPumqc8P12v6WSGaGNa9X7LFtc5H088QagAAOM7GP/9Gvtr9srgyFZuSpaP/fEUXHnxJ64fO0VlXz1N+YmhnjK9paJa32dTglCDv0G2arbc5OFzRGmRGTGidc6ZuT2svTT+8GpdQAwCApOYmr957/WmdteUe+WVRrPHVnGP/SP6WCmYtDMv7VrkbJUkDnaeY16b6Q+mtB1sn0TtSJR36VPry1jwB8QOkhoPSsHFhqbW3I9QAACBp58Y3dN7GW+WXoY+ufF65o8fqvf//SX1j289lGfrNsL3vgS9DTcapQs0Hf5G2v9T6/egZ0sgpUtowKfm01vs8xSRKmWe1TrZ32kVhq7c3I9QAACBpf1W1zpR06D//odOz/1WSdNYVN2pLYorOvvx7YXvf6i9DTXqS4+QrHq356vvh46WzvvvV81krw1BZ30OoAQBA0vufHVChpJTU9MCyHX97Sa4Nv9LHm5ao5d/madiYy/Xq9mqt+rBGW3bXqrahSbF2q5JibV8+7F/7apPzuO+THG1fd8batb+2UakJdtmtp5gP9/Cur74fdEZ4fgh9HKEGAABJF2THS9slm+OrGzy2bF+pRL9bLZYYjSyfKZVLcb7zVfsv9+l738hWepJDjc0+HWlskbuxRfWNLapvbNbBeq92HTzy5fPWZc0+s933dahRYzNrpNo9rQN9YxIka8yJA31rdkmjpkoTF0mu/jexXmcQagAAkGQ3vfKbhkxZtHvHRlWt+a3OrH1LO1IvV94PHpUeGy5JOve7P1PRmWODats0TXlb/IGAc3zYSdt0p87cvUL69Z1fbWCxSfaEr0JOTLx06BPp7H8n0JwEoQYAAElJg8+UuV069MAI5eqwXHLqn5nf1ohpd8qVmi6N/an0wUoNOvPSoNs2DEOxdqti7dYTxs7s3rRP8UMvk8b+RGryfPk4IjU1HPe9Rxp0pnT6lSHa2+hEqAEAQNIZY7+lbS3L1PjuH7VvZJHOuPS7utBx3BVJ9QekpIyOG+jIjpelw59Ko66SUnNPfL1unzT6GmnY5V2uHa0INQAAfGn0JVOlS6a2/6LnC6lqu/SHq6RpS6SkQSeuU/NZ6zr2+K8mwnvx+tbXGt3S+J+3Xb/F2zrnTMqQEO5F/0WoCQNT7Q8GAwD0YQVzpC1Pt/a81O1tP9T85SfSrr+1v/3bj7du70hqnVPG4ZSsNkkmoSZECDUAAHTGiAmtA3h3vCw98+3WcHKsRyYmofX7fe9KF9wkXfjj1pl/mxqkhLTWy7G/2Cl566Wm+tav3iOSt17m0EtbJ81DtxFqAADorOwLpCsfaZ0Ir6nhy+Di+SrAZF8gjZ4upZzWdruUIdKwy9pt0nDvlmJDdDPLfo5QEwaG+t9NxACgX4iJl867PtJVoAOnmL4QXcGYGgBAZ/hNP78IhxChBgCACHF73XLFuiJdRtQg1AAAECH1TfVKsidFuoyoQagJA04/AQA6y/j6PZ7QZYSaMOD8KACgM/glOLQINQAAICoQagAAQFQg1AAAgKgQVKgpLS1Vfn6+nE6nnE6nCgoKtGrVqsDrFRUVmjZtmtLT0+V0OjVjxgxVVVWdtM0hQ4bIMIwTHnPmzAmsc+mll57w+k033RTkrgIAgGgWVKgZPHiwFi9erM2bN2vTpk26/PLLNXXqVG3fvl0ej0eFhYUyDEPl5eVat26dmpqaNHnyZPn9/g7b3LhxoyorKwOP119/XZI0ffr0NuvdcMMNbdZ76KGHurC7AAD0HlxYElpB3SZh8uTJbZ4vWLBApaWleuedd7Rv3z599tln2rJli5zO1ntYLFu2TCkpKSovL9f48ePbbTM9Pb3N88WLF2vYsGH65je/2WZ5fHy8MjIygikXAIBeyzS58inUujymxufz6bnnnpPH41FBQYG8Xq8Mw5DD4QisExsbK4vForVr13aqzaamJj3zzDO6/vrrT7hu/9lnn9WAAQN05plnqqSkRA0NDSdty+v1yu12t3kAANBb1HprmU04xIK+oeW2bdtUUFCgxsZGJSYmasWKFRo1apTS09OVkJCgefPmaeHChTJNU/Pnz5fP51NlZWWn2v7zn/+s2tpaXXfddW2W//u//7tOO+00ZWVl6f3339e8efO0c+dOvfTSSx22tWjRIt17773B7h4AAD2ivqleOc6cSJcRVQwzyP6vpqYm7d69W3V1dXrxxRf1u9/9Tm+99ZZGjRql1157TbNnz9auXbtksVj0ve99Tzt27NA3vvENlZaWnrLtiRMnKiYmRitXrjzpeuXl5Ro3bpw++eQTDRs2rN11vF6vvF5v4Lnb7VZ2drbq6uoCp8fCZbd7N39QAQAnxWdF57jdbrlcrk59fgfdUxMTE6Phw4dLksaMGaONGzfq0Ucf1ZIlS1RYWKiKigodPHhQNptNycnJysjI0NChQ0/Z7ueff6433njjpL0vx1xwwQWSdNJQ43A42pwKAwAA0S3oUPN1fr+/TY+IJA0YMEBSa49KdXW1pkyZcsp2li5dqoEDB+qKK6445bpbt26VJGVmZgZfMAAAvQBXPoVeUKGmpKRERUVFysnJUX19vZYvX641a9aorKxMUmswGTlypNLT07V+/XrdfPPNKi4uVl5eXqCNcePGadq0aZo7d25gmd/v19KlSzVr1izZbG1Lqqio0PLly/Wtb31LaWlpev/991VcXKxLLrlE+fn53dl3AAAihvs+hV5Qoaa6ulozZ85UZWWlXC6X8vPzVVZWpgkTJkiSdu7cqZKSEh0+fFhDhgzRXXfdpeLi4jZtHDs9dbw33nhDu3fv1vXXX3/Ce8bExOiNN97Qr3/9a3k8HmVnZ+vqq6/Wz372s2D3tceQvgEAp8JnRegFPVC4rwpmoFF3MfgLAHAqfFZ0TjCf39z7CQCAHuY3/fTUhAGhJgz4gwoAOJn6pnolxSRFuoyoQ6gJAwZ/AQBOxt3kJtSEAaEGAIAeZpqmrBZrpMuIOoSaMDBkcKMyAAB6GKEmDKwWq1rMlkiXAQBAv0KoCQO7xa4WP6EGAICeRKgJA5vFpmZ/c6TLAACgXyHUhAE9NQAA9DxCTRjYLDY1++ipAQCciAtJwodQEwZ2i52BwgCAdrmb3HI5XJEuIyoRasLAarHK7/dHugwAQC9U562TMya89yDsrwg1AAD0MMPgdjrhQKgBAABRgVATJtz/CQDQHp/pi3QJUYtQAwBAD7Ia3PMpXAg1YWKI86UAgBPRkx8+hBoAABAVCDUAACAqEGoAAOgh9U31SrQnRrqMqEWoAQCgh9Q01igtLi3SZUQtQg0AAD2Ei0jCi1ADAEAP4cqn8CLUAADQQ+ipCS9CDQAAPYSemvAi1AAAgKhAqAEAAFGBUAMAAKICoQYAgB7Q5GuS3WKPdBlRjVADAEAPONx4WKlxqZEuI6oRagAA6AFNviY5rI5IlxHVCDUAACAqEGoAAEBUINQAAICoQKgBAABRgVADAACiAqEGAABEBUINAACICkGFmtLSUuXn58vpdMrpdKqgoECrVq0KvF5RUaFp06YpPT1dTqdTM2bMUFVV1UnbHDJkiAzDOOExZ86cwDqNjY2aM2eO0tLSlJiYqKuvvvqU7QIAgP4lqFAzePBgLV68WJs3b9amTZt0+eWXa+rUqdq+fbs8Ho8KCwtlGIbKy8u1bt06NTU1afLkyfL7/R22uXHjRlVWVgYer7/+uiRp+vTpgXWKi4u1cuVKvfDCC3rrrbe0f/9+ffvb3+7iLgMA0PMMGZEuIeoZpmma3WkgNTVVDz/8sLKzs1VUVKSamho5nU5JUl1dnVJSUvTaa69p/PjxnWrvlltu0SuvvKKPP/5YhmGorq5O6enpWr58ub7zne9Ikj788EONHDlS69ev14UXXtipdt1ut1wul+rq6gL1hdMe9x5lO7PD/j4AgL6Bz4WuCebzu8tjanw+n5577jl5PB4VFBTI6/XKMAw5HF9NAR0bGyuLxaK1a9d2qs2mpiY988wzuv7662UYrYl28+bNam5ubhOKTj/9dOXk5Gj9+vUdtuX1euV2u9s8AACIhIbmBsXaYiNdRtQLOtRs27ZNiYmJcjgcuummm7RixQqNGjVKF154oRISEjRv3jw1NDTI4/Hotttuk8/nU2VlZafa/vOf/6za2lpdd911gWUHDhxQTEyMkpOT26w7aNAgHThwoMO2Fi1aJJfLFXhkZ5OOAQCRUeOtUXJscqTLiHpBh5q8vDxt3bpVGzZs0OzZszVr1izt2LFD6enpeuGFF7Ry5UolJibK5XKptrZW5557riyWzr3N73//exUVFSkrKyvoHfm6kpIS1dXVBR579uzpdpsAAHSF3++X3WKPdBlRzxbsBjExMRo+fLgkacyYMdq4caMeffRRLVmyRIWFhaqoqNDBgwdls9mUnJysjIwMDR069JTtfv7553rjjTf00ksvtVmekZGhpqYm1dbWtumtqaqqUkZGRoftORyONqfCAACIFFPdGr6KTur2PDV+v19er7fNsgEDBig5OVnl5eWqrq7WlClTTtnO0qVLNXDgQF1xxRVtlo8ZM0Z2u12rV68OLNu5c6d2796tgoKC7pYPAACiRFA9NSUlJSoqKlJOTo7q6+u1fPlyrVmzRmVlZZJag8nIkSOVnp6u9evX6+abb1ZxcbHy8vICbYwbN07Tpk3T3LlzA8v8fr+WLl2qWbNmyWZrW5LL5dIPf/hD/dd//ZdSU1PldDr1k5/8RAUFBZ2+8gkAgEjicu6eEVSoqa6u1syZM1VZWSmXy6X8/HyVlZVpwoQJklp7UEpKSnT48GENGTJEd911l4qLi9u0cez01PHeeOMN7d69W9dff3277/vII4/IYrHo6quvltfr1cSJE/W///u/wZQOAEDEcPqpZ3R7npq+gnlqAACRstu9WznOnEiX0Sf1yDw1ODlSOQDgGE4/9QxCTZjwBxgAcAy/6PYMQk0Y9ZMzewAA9AqEmjCxW+1q8bdEugwAQIQ1+5rpve8hhJowsVlsavY3R7oMAECE1XprlRaXFuky+gVCTZjYDEINAEA62nJU8fb4SJfRLxBqwsRmsXH6CQCAHkSoCRO71U5PDQAAPYhQEyZWwyqf6Yt0GQAA9BuEmjCxGlb5/f5IlwEAQL9BqAkTw+DyPQAAehKhJoyYQRIAgJ5DqAEAAFGBUBNGzCAJAEDPIdSEEaefAADoOYQaAADChBsb9yxCDQAAYeJucispJinSZfQbhBoAAMLE7XXLGeOMdBn9BqEGAIAw8csvq8Ua6TL6DUINAABhwlWwPYtQAwAAogKhBgAARAVCDQAAiAqEGgAAwsBv+rm5cQ8j1AAAEAaHjh5SWmxapMvoVwg1AACEwdGWo4q3x0e6jH6FUAMAAKICoQYAAEQFQg0AAIgKhJowMsXdWQEA6CmEGgAAEBUINWHEPT8AAOg5hBoAABAVCDUAACAqEGrCyGf6Il0CAAD9BqEmjGyGLdIlAAAioLGlUTHWmEiX0e8QagAACLGaxhqlxqZGuox+J6hQU1paqvz8fDmdTjmdThUUFGjVqlWB1ysqKjRt2jSlp6fL6XRqxowZqqqqOmW7+/bt0/e//32lpaUpLi5Oo0eP1qZNmwKvX3fddTIMo81j0qRJwZQOAECPafY301MTAUGFmsGDB2vx4sXavHmzNm3apMsvv1xTp07V9u3b5fF4VFhYKMMwVF5ernXr1qmpqUmTJ0+W3+/vsM2amhpddNFFstvtWrVqlXbs2KFf/vKXSklJabPepEmTVFlZGXj88Y9/7NoeAwCAqBTUoI/Jkye3eb5gwQKVlpbqnXfe0b59+/TZZ59py5YtcjqdkqRly5YpJSVF5eXlGj9+fLttPvjgg8rOztbSpUsDy3Jzc09Yz+FwKCMjI5hyAQBAP9LlMTU+n0/PPfecPB6PCgoK5PV6ZRiGHA5HYJ3Y2FhZLBatXbu2w3b+8pe/6LzzztP06dM1cOBAnXPOOfrtb397wnpr1qzRwIEDlZeXp9mzZ+vQoUNdLR0AAEShoEPNtm3blJiYKIfDoZtuukkrVqzQqFGjdOGFFyohIUHz5s1TQ0ODPB6PbrvtNvl8PlVWVnbY3qeffqrS0lKNGDFCZWVlmj17tn76059q2bJlgXUmTZqkP/zhD1q9erUefPBBvfXWWyoqKpLP1/El016vV263u80DAABEL8M0zaDuutjU1KTdu3errq5OL774on73u9/prbfe0qhRo/Taa69p9uzZ2rVrlywWi773ve9px44d+sY3vqHS0tJ224uJidF5552nt99+O7Dspz/9qTZu3Kj169e3u82nn36qYcOG6Y033tC4cePaXee///u/de+9956wvK6uLnB6LNz2uPco25ndI+8FAOg9drt3K8eZE+kyooLb7ZbL5erU53fQPTUxMTEaPny4xowZo0WLFumss87So48+KkkqLCxURUWFqqurdfDgQT399NPat2+fhg4d2mF7mZmZGjVqVJtlI0eO1O7duzvcZujQoRowYIA++eSTDtcpKSlRXV1d4LFnz54g9xQAAPQl3Z4dzu/3y+v1tlk2YMAASVJ5ebmqq6s1ZcqUDre/6KKLtHPnzjbLPvroI5122mkdbrN3714dOnRImZmZHa7jcDjajO+JBFNBdYIBAIBuCKqnpqSkRH/729/02Wefadu2bSopKdGaNWt07bXXSpKWLl2qd955RxUVFXrmmWc0ffp0FRcXKy8vL9DGuHHj9Jvf/CbwvLi4WO+8844WLlyoTz75RMuXL9eTTz6pOXPmSJKOHDmi22+/Xe+8844+++wzrV69WlOnTtXw4cM1ceLEUPwMAABAFAiqp6a6ulozZ85UZWWlXC6X8vPzVVZWpgkTJkiSdu7cqZKSEh0+fFhDhgzRXXfdpeLi4jZtVFRU6ODBg4Hn559/vlasWKGSkhLdd999ys3N1a9//etAULJarXr//fe1bNky1dbWKisrS4WFhbr//vsj3hNzKoaMSJcAAOhhQQ5VRQgFPVC4rwpmoFGoMFAYAPofd5NbPr9PKbEpp14ZpxTWgcIAAKBjdY11cjlckS6jXyLUhBEDhQGg/zFlymLw8RoJ/NQBAAghUybjaiKEUBNGDBQGgP6nydckv9nxjZwRPoQaAABCKMYaI6vFGuky+iVCTRgxpgYA+h966SOHUAMAAKICoQYAAEQFQk0Y0QUJAEDPIdQAABBC/EIbOYSaMGKgMAAAPYdQE2ZMwAQA/UdDc4NibbGRLqPfItSEkcWwMAETAPQjtd5aJccmR7qMfotQE0Y2i00+0xfpMgAAPcTn98lusUe6jH6LUBNGNotNLf6WSJcBAEC/QKgJI6thVYtJqAEAoCfYIl1ANLNarPL5Of0EAP2GIe1275YkZSdlyzC4vLsn0VMTRjaDMTUA0J9kJ2Urx5kT6TL6LUJNGDGmBgD6L3ppeh6hJoyshpVQAwD9ELMKRwahJoyYpwYAgJ5DqAkjuh4BoH/iNjmRQagJM7ogAQDoGYSaMCOtA0D/Ypomv9BGCKEGAIAQqvPWyelwRrqMfolQAwBACNU31csZQ6iJBEINAAAhZMrkQpEIIdQAABBCjKeJHEINAAAhxAUikUOoAQAAUYFQAwBACHH6KXIINQAAhBCnnyKHUAMAAKICoQYAgBDi9FPkEGoAAAgRv+mPdAn9GqEGAIAQqfXWyhXrinQZ/RahBgCAEPE0eZRkT4p0Gf0WoQYAgBDhFgmRFVSoKS0tVX5+vpxOp5xOpwoKCrRq1arA6xUVFZo2bZrS09PldDo1Y8YMVVVVnbLdffv26fvf/77S0tIUFxen0aNHa9OmTYHXTdPUPffco8zMTMXFxWn8+PH6+OOPgykdAABEuaBCzeDBg7V48WJt3rxZmzZt0uWXX66pU6dq+/bt8ng8KiwslGEYKi8v17p169TU1KTJkyfL7+944FRNTY0uuugi2e12rVq1Sjt27NAvf/lLpaSkBNZ56KGH9Nhjj+mJJ57Qhg0blJCQoIkTJ6qxsbHrew4AAKKKYZpmt2YJSk1N1cMPP6zs7GwVFRWppqZGTmfrLdfr6uqUkpKi1157TePHj293+/nz52vdunX6+9//3u7rpmkqKytLt956q2677bZAu4MGDdJTTz2la665plN1ut1uuVwu1dXVBerrCbvdu5XjzOmx9wMARA7/5odeMJ/fXR5T4/P59Nxzz8nj8aigoEBer1eGYcjhcATWiY2NlcVi0dq1azts5y9/+YvOO+88TZ8+XQMHDtQ555yj3/72t4HXd+3apQMHDrQJRS6XSxdccIHWr1/fYbter1dut7vNAwAARK+gQ822bduUmJgoh8Ohm266SStWrNCoUaN04YUXKiEhQfPmzVNDQ4M8Ho9uu+02+Xw+VVZWdtjep59+qtLSUo0YMUJlZWWaPXu2fvrTn2rZsmWSpAMHDkiSBg0a1Ga7QYMGBV5rz6JFi+RyuQKP7OzsYHcVAAD0IUGHmry8PG3dulUbNmzQ7NmzNWvWLO3YsUPp6el64YUXtHLlSiUmJsrlcqm2tlbnnnuuLJaO38bv9+vcc8/VwoULdc455+jGG2/UDTfcoCeeeKJbO1ZSUqK6urrAY8+ePd1qDwAA9G62YDeIiYnR8OHDJUljxozRxo0b9eijj2rJkiUqLCxURUWFDh48KJvNpuTkZGVkZGjo0KEdtpeZmalRo0a1WTZy5Ej96U9/kiRlZGRIkqqqqpSZmRlYp6qqSmeffXaH7TocjjanwgAACKduDlFFCHR7nhq/3y+v19tm2YABA5ScnKzy8nJVV1drypQpHW5/0UUXaefOnW2WffTRRzrttNMkSbm5ucrIyNDq1asDr7vdbm3YsEEFBQXdLR8AgJBwN7nlcjCbcCQF1VNTUlKioqIi5eTkqL6+XsuXL9eaNWtUVlYmSVq6dKlGjhyp9PR0rV+/XjfffLOKi4uVl5cXaGPcuHGaNm2a5s6dK0kqLi7W2LFjtXDhQs2YMUP/+Mc/9OSTT+rJJ5+UJBmGoVtuuUUPPPCARowYodzcXN19993KysrSVVddFaIfAwAA3VPnrVN2EuM3IymoUFNdXa2ZM2eqsrJSLpdL+fn5Kisr04QJEyRJO3fuVElJiQ4fPqwhQ4borrvuUnFxcZs2jp2eOub888/XihUrVFJSovvuu0+5ubn69a9/rWuvvTawzh133CGPx6Mbb7xRtbW1uvjii/Xqq68qNja2O/sOAEBIMZtwZHV7npq+gnlqAADhxL/34dEj89QAAAD0JoSaMPOZvkiXAABAvxD0Jd0ITnpcuna7d7dZZqr1jJ8ho80yi2GRxbAEXjNkBM7PHvu+o2XHP5f01bpfX6edNkyZavG3qMXfomZ/s1r8LTJNM7CuxbC0+f5Yjce+P9Ze4Ptj+xX4YrT9apxYLwAA3UWoCbPEmEQlxiR2al2f3yfzy/9a///yP/Orr8e0WX7ca8eWBbY/9vy4145fdozNYpPdYlecLU42i00WWWTKlN/0y2+23pDUb/rll791e9NUs9ksv+kPtHfs+2P1tfn6teXH6uurmv3NirHEtBtQv66j/Wxvm778MwmWKbPDn9vxPwebxSZXjEsJ9gRCMICTItT0IlaLNdIlAL1Os69Z7ia3ahprTgh9x0LR1wPS8evF2+OVaE+Uw+ogFAFRjlADoFezW+1Ki0tTWlxa0NuapqmjLUdV561Tk6+pdVk7vWHHTsN2JN4er6SYJDmszFKO9vWTC4l7PUINgKhlGIbi7fGKt8d3uQ3TNNXQ0qCaxhp5fd6Tnmpss91xIclusSspJkkJ9oTAmDREl/rm+k4PNUD4EGoA4CQMw1CCPUEJ9oQut9Hsa1Z9c7321e/r0rgpm8XWOj7Pnkgo6qVqG2s1OGlwpMvo9wg1ABBmdqtdqdZUpcamdmn740ORX/5O9xYdY7VYlRSTRCgKo2NXsCKyCDUA0Mt1OxT5m3Wk6Yj2Hdl3wtWPnQlIFsMSCEVc0NC+YIMmwoNQAwBRzm6xKyU2RSmxKV3avsXfoiNNR7Tfs/+EAbEdXZHWZplhyBnjJBQh7Ag1AICTsllsSo5NVnJscpe2b/G3yNPsUaWnMjDvVUfam/vJMAwl2ZOUGJMom4WPLXSMPx0AgLCyWWxyOVxyOVxd2t7n9+lI8xEd8Bw4ZSg63rGAZJElMBGq3WLvUg3oGwg1AIBezWqxdjsUeVo8qm6ols8f/P34DBlKiElQUkwSoaiXI9QAAKKa1WKVM8YpZ4yzS9sfH4pa/C3tjhtiYsbegVADAMBJdDcUoedwUT0AAIgKhBoAABAVCDUAACAqEGoAAEBUINQAAICoQKgBAABRgVADAACiAqEGAABEBUINAACICoQaAAAQFQg1AAAgKhBqAABAVCDUAACAqECoAQAAUcEW6QJ6immakiS32x3hSgAAQGcd+9w+9jl+Mv0m1NTX10uSsrOzI1wJAAAIVn19vVwu10nXMczORJ8o4Pf7tX//fiUlJckwjBNed7vdys7O1p49e+R0OiNQIU6G49N7cWx6N45P78bxOTXTNFVfX6+srCxZLCcfNdNvemosFosGDx58yvWcTid/sHoxjk/vxbHp3Tg+vRvH5+RO1UNzDAOFAQBAVCDUAACAqECo+ZLD4dDPf/5zORyOSJeCdnB8ei+OTe/G8endOD6h1W8GCgMAgOhGTw0AAIgKhBoAABAVCDUAACAqEGoAAEBU6Deh5qOPPtLUqVM1YMAAOZ1OXXzxxXrzzTfbXffQoUMaPHiwDMNQbW3tSdsdMmSIDMNo81i8eHEY9iC6hev4HOP1enX22WfLMAxt3bo1dIX3E+E6PlOmTFFOTo5iY2OVmZmpH/zgB9q/f38Y9iC6heP4fPbZZ/rhD3+o3NxcxcXFadiwYfr5z3+upqamMO1FdArX350FCxZo7Nixio+PV3JycugL76P6Tai58sor1dLSovLycm3evFlnnXWWrrzySh04cOCEdX/4wx8qPz+/023fd999qqysDDx+8pOfhLL0fiGcx0eS7rjjDmVlZYWq3H4nXMfnsssu0/PPP6+dO3fqT3/6kyoqKvSd73wn1OVHvXAcnw8//FB+v19LlizR9u3b9cgjj+iJJ57QnXfeGY5diFrh+rvT1NSk6dOna/bs2aEuuW8z+4EvvvjClGT+7W9/Cyxzu92mJPP1119vs+7//u//mt/85jfN1atXm5LMmpqak7Z92mmnmY888kgYqu4/wnl8TNM0//rXv5qnn366uX37dlOSuWXLlhDvQXQL9/E53ssvv2wahmE2NTWFovR+oSePz0MPPWTm5uaGoux+oSeOzdKlS02XyxXCqvu2ftFTk5aWpry8PP3hD3+Qx+NRS0uLlixZooEDB2rMmDGB9Xbs2KH77rtPf/jDH05506zjLV68WGlpaTrnnHP08MMPq6WlJRy7EbXCeXyqqqp0ww036Omnn1Z8fHy4diGqhfvvzzGHDx/Ws88+q7Fjx8put4dyF6JaTx0fSaqrq1NqamqoSo96PXls8KVIp6qesmfPHnPMmDGmYRim1Wo1MzMzzXfffTfwemNjo5mfn28+/fTTpmma5ptvvtmptPzLX/7SfPPNN8333nvPLC0tNZOTk83i4uJw7kpUCsfx8fv95qRJk8z777/fNE3T3LVrFz01XRSuvz+maZp33HGHGR8fb0oyL7zwQvPgwYPh2o2oFc7jc8zHH39sOp1O88knnwx1+VEt3MeGnpq2+nQknD9//gmDdL/++PDDD2WapubMmaOBAwfq73//u/7xj3/oqquu0uTJk1VZWSlJKikp0ciRI/X9738/qBr+67/+S5deeqny8/N100036Ze//KUef/xxeb3ecOxynxLp4/P444+rvr5eJSUl4drFPi3Sx+eY22+/XVu2bNFrr70mq9WqmTNnymSi815zfCRp3759mjRpkqZPn64bbrghlLvZJ/WmY4OviWSi6q7q6mrzgw8+OOnD6/Wab7zxhmmxWMy6uro22w8fPtxctGiRaZqmedZZZ5kWi8W0Wq2m1Wo1LRaLKcm0Wq3mPffc0+ma/vnPf5qSzA8//DCk+9oXRfr4TJ06tc02Vqs1sM3MmTPDvv+9XaSPT3v27NljSjLffvvtkO5rX9Rbjs++ffvMESNGmD/4wQ9Mn88Xtv3tS3rLsTFNemq+zha5ONV96enpSk9PP+V6DQ0NknTCuUqLxSK/3y9J+tOf/qSjR48GXtu4caOuv/56/f3vf9ewYcM6XdPWrVtlsVg0cODATm8TrSJ9fB577DE98MADgef79+/XxIkT9X//93+64IILgt6faBPp49OeY+3R09k7js++fft02WWXacyYMVq6dCnjPb7UG44NOhDpVNUTvvjiCzMtLc389re/bW7dutXcuXOnedttt5l2u93cunVru9u0d15zw4YNZl5enrl3717TNE3z7bffNh955BFz69atZkVFhfnMM8+Y6enp9AIEKVzH5+sYU9M14To+77zzjvn444+bW7ZsMT/77DNz9erV5tixY81hw4aZjY2NPbFrUSFcx2fv3r3m8OHDzXHjxpl79+41KysrAw90Tjj/bfv888/NLVu2mPfee6+ZmJhobtmyxdyyZYtZX18f7t3q1fpFqDFN09y4caNZWFhopqammklJSeaFF15o/vWvf+1w/fb+YB1btmvXLtM0TXPz5s3mBRdcYLpcLjM2NtYcOXKkuXDhQv5B7oJwHJ+vI9R0XTiOz/vvv29edtllZmpqqulwOMwhQ4aYN910U4ehFB0Lx/FZunSpKandBzovXP+2zZo1q91j8+abb4ZvZ/oAwzQZkQcAAPo+TpACAICoQKgBAABRgVADAACiAqEGAABEBUINAACICoQaAAAQFQg1AAAgKhBqAABAVCDUAACAqECoAQAAUYFQAwAAogKhBgAARIX/Bw8DiqO9DBOrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"showing the counties where we assigned BGs missing from original COI list\")\n",
    "for c in range(nCounties):\n",
    "    LIST = set(missedBGlist).intersection(set(countyTractList[c]))\n",
    "    if len(LIST) > 0:\n",
    "        for i,b in enumerate(LIST):\n",
    "            plotPoly(tractGeom[b])\n",
    "            plotCenter(b,tractGeom[b])\n",
    "            plotPoly(COIgeom[tractMuniNo[b]])\n",
    "        plotPoly(countyGeom[c],0.1)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "be4c1dbd-15a1-45dd-b185-d3af5522b1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a histogram of number of bgs per COI\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the minimum number of bgs in a COI is 1\n"
     ]
    }
   ],
   "source": [
    "print(\"here is a histogram of number of bgs per COI\")\n",
    "plt.hist([len(muniTractList[u]) for u in range(nCOIs)],bins = [0,1,3,10,30,300,1000] )\n",
    "plt.show()\n",
    "plt.scatter([u for u in range(nCOIs)], [len(muniTractList[u]) for u in range(nCOIs)] )\n",
    "plt.show()\n",
    "print(\"the minimum number of bgs in a COI is\",np.min([len(muniTractList[u]) for u in range(nCOIs)]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "969dbd0a-31fa-43ed-9dd0-2d9ba9df1c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now determine the county topology\n"
     ]
    }
   ],
   "source": [
    "print(\"Now determine the county topology\")\n",
    "countyNeighbors = [list() for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1, nCounties):\n",
    "        if isRookAdj(countyGeom[c],countyGeom[cc]):\n",
    "            countyNeighbors[c].append(cc)\n",
    "            countyNeighbors[cc].append(c)\n",
    "mainMAP = countyMAP.geoms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf28a7ff-6138-42fe-9e38-9c1e11b2ebf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Option 1: we read in the bg topology\n",
      "I read in the blockgroup topology; there are 9472 blockgroups and state pop of 11799448\n"
     ]
    }
   ],
   "source": [
    "print(\"Option 1: we read in the bg topology\")  #vs creating ourselves, connecting Kelley to mainland\n",
    "infile = \"./state_map_files/OH99mod_bgTopologies_17Nov24.csv\"\n",
    "bgDF = pd.read_csv(infile)\n",
    "tractCPx = bgDF[\"centroid x\"]\n",
    "tractCPy = bgDF[\"centroid y\"]\n",
    "tractPop = bgDF[\"bgPop\"]\n",
    "bgNbrList = bgDF[\"bgNbrs\"]\n",
    "countyNo = bgDF[\"countyNo\"]\n",
    "isBorderBG = bgDF[\"isBorderBG\"]\n",
    "borderBGs = list()\n",
    "for b in range(len(isBorderBG)):\n",
    "    if isBorderBG[b] == 1:\n",
    "        borderBGs.append(b)\n",
    "nTracts = len(tractPop)\n",
    "statePop = np.sum(tractPop)\n",
    "#bgNbrs = bgNbrList.copy()\n",
    "bgNbrs = [ast.literal_eval(bgNbrList[t]) for t in range(nTracts) ]\n",
    "print(\"I read in the blockgroup topology; there are\",nTracts,\"blockgroups and state pop of\",statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4bdfdde5-bf1e-4710-9115-70567c5c8fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compute and plot the COI pops by area\n"
     ]
    },
    {
     "data": {
      "image/png": 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srAxJSUkIDAxEWFgYFixYgPr6eoeYHTt2YNSoUdBoNBg6dCgyMzOblGflypUYOHAg/P39ER8fj/z8fIfz1dXVSE5ORp8+fdCrVy9MmzYNFRUVrnxkIiIicpO4QSGI0PmjpUnyCjTMHosbFOK1MrmUCO3cuRPJycnYu3cvcnJyUFdXhwkTJqCqqsohbtasWSgvL5cey5Ytk85ZrVYkJSWhtrYWe/bswbp165CZmYm0tDQpprS0FElJSRg3bhwKCwsxb948PPXUU8jOzpZiNmzYgNTUVKSnp+PAgQMYOXIkEhMTcfbsWSlm/vz52LRpEzZu3IidO3fizJkzeOihh1yuJCIiIuo4lVKB9MkGAGiSDNmfp082eHU9IYUQot1Ds8+dO4ewsDDs3LkTY8eOBdDQIhQTE4O33nqr2dd88803+MUvfoEzZ84gPDwcALB69WosXLgQ586dg1qtxsKFC/H111+jqKhIet2MGTNQWVmJrKwsAEB8fDzuuOMOvPPOOwAAm82GqKgoPPPMM1i0aBHMZjP69u2Ljz76CL/61a8AAMeOHcPNN9+MvLw8jB49us3PZ7FYoNPpYDabodVq21tNRERE1Iin1xFy5f7doTFCZrMZABAS4tiE9eGHHyI0NBTR0dFYvHgxrl69Kp3Ly8vDiBEjpCQIABITE2GxWHDkyBEpJiEhweGaiYmJyMvLAwDU1taioKDAIUapVCIhIUGKKSgoQF1dnUPM8OHD0b9/fynmejU1NbBYLA4PIiIicq+J0RH4buF9+HjWaCyfEYOPZ43Gdwvv8/piikAHVpa22WyYN28e7rrrLkRHR0vHH3nkEQwYMACRkZE4dOgQFi5ciJKSEnz22WcAAJPJ5JAEAZCem0ymVmMsFguuXbuGS5cuwWq1Nhtz7Ngx6RpqtRrBwcFNYuzvc72MjAwsXbrUxZogIiIiV6mUChiH9PF1MdqfCCUnJ6OoqAjfffedw/HZs2dL/z1ixAhERERg/PjxOHnyJIYMGdL+knrB4sWLkZqaKj23WCyIioryYYmIiIjIk9rVNZaSkoLNmzdj+/bt6NevX6ux8fHxAIATJ04AAPR6fZOZW/bner2+1RitVouAgACEhoZCpVI1G9P4GrW1taisrGwx5noajQZardbhQURERN2XS4mQEAIpKSn4/PPPsW3bNgwaNKjN1xQWFgIAIiIa+v2MRiMOHz7sMLsrJycHWq0WBoNBisnNzXW4Tk5ODoxGIwBArVYjNjbWIcZmsyE3N1eKiY2NhZ+fn0NMSUkJysrKpBgiIiKSOeGCuXPnCp1OJ3bs2CHKy8ulx9WrV4UQQpw4cUK89NJLYv/+/aK0tFR8+eWXYvDgwWLs2LHSNerr60V0dLSYMGGCKCwsFFlZWaJv375i8eLFUswPP/wgAgMDxYIFC8TRo0fFypUrhUqlEllZWVLM+vXrhUajEZmZmaK4uFjMnj1bBAcHC5PJJMXMmTNH9O/fX2zbtk3s379fGI1GYTQanf68ZrNZABBms9mVaiIiIiIfcuX+7VIihIZtQJo81q5dK4QQoqysTIwdO1aEhIQIjUYjhg4dKhYsWNCkIKdOnRIPPPCACAgIEKGhoeK5554TdXV1DjHbt28XMTExQq1Wi8GDB0vv0diKFStE//79hVqtFnFxcWLv3r0O569duyaefvpp0bt3bxEYGCgefPBBUV5e7vTnZSJERETU9bhy/+7QOkLdHdcRIiIi6nq8to4QERERUVfGRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLZ6+LoARCQ/VptAfulFnL1cjbAgf8QNCoFKqfB1sYhIhpgIEZFXZRWVY+mmYpSbq6VjETp/pE82YGJ0hA9LRkRyxK4xIvKarKJyzP3ggEMSBAAmczXmfnAAWUXlPioZEckVEyEi8gqrTWDppmKIZs7Zjy3dVAyrrbkIIiLPYCJERF6RX3qxSUtQYwJAubka+aUXvVcoIpI9JkJE5BVnL7ecBLUnjojIHZgIEZFXhAX5uzWOiMgdmAgRkVfEDQpBhM4fLU2SV6Bh9ljcoBBvFouIZI6JEBF5hUqpQPpkAwA0SYbsz9MnG7ieEBF5FRMhIvKaidERWPXYKOh1jt1fep0/Vj02iusIEZHXcUFFIvKqidERuN+g58rSRNQpMBEiIq9TKRUwDunj62IQEbFrjIiIiOSLiRARERHJFhMhIiIiki0mQkRERCRbLiVCGRkZuOOOOxAUFISwsDBMnToVJSUlDjHV1dVITk5Gnz590KtXL0ybNg0VFRUOMWVlZUhKSkJgYCDCwsKwYMEC1NfXO8Ts2LEDo0aNgkajwdChQ5GZmdmkPCtXrsTAgQPh7++P+Ph45Ofnu1wWIiIiki+XEqGdO3ciOTkZe/fuRU5ODurq6jBhwgRUVVVJMfPnz8emTZuwceNG7Ny5E2fOnMFDDz0knbdarUhKSkJtbS327NmDdevWITMzE2lpaVJMaWkpkpKSMG7cOBQWFmLevHl46qmnkJ2dLcVs2LABqampSE9Px4EDBzBy5EgkJibi7NmzTpeFiIiIZE50wNmzZwUAsXPnTiGEEJWVlcLPz09s3LhRijl69KgAIPLy8oQQQmzZskUolUphMpmkmFWrVgmtVitqamqEEEK88MIL4pZbbnF4r+nTp4vExETpeVxcnEhOTpaeW61WERkZKTIyMpwuS1vMZrMAIMxms1PxRERE5Huu3L87NEbIbDYDAEJCGvYGKigoQF1dHRISEqSY4cOHo3///sjLywMA5OXlYcSIEQgPD5diEhMTYbFYcOTIESmm8TXsMfZr1NbWoqCgwCFGqVQiISFBinGmLNerqamBxWJxeBAREVH31e5EyGazYd68ebjrrrsQHR0NADCZTFCr1QgODnaIDQ8Ph8lkkmIaJ0H28/ZzrcVYLBZcu3YN58+fh9VqbTam8TXaKsv1MjIyoNPppEdUVJSTtUFERERdUbsToeTkZBQVFWH9+vXuLI9PLV68GGazWXr8+OOPvi4SEREReVC7tthISUnB5s2bsWvXLvTr1086rtfrUVtbi8rKSoeWmIqKCuj1einm+tld9plcjWOun91VUVEBrVaLgIAAqFQqqFSqZmMaX6OtslxPo9FAo9G4UBNERETUlbnUIiSEQEpKCj7//HNs27YNgwYNcjgfGxsLPz8/5ObmSsdKSkpQVlYGo9EIADAajTh8+LDD7K6cnBxotVoYDAYppvE17DH2a6jVasTGxjrE2Gw25ObmSjHOlIWIiIhkzpVR2HPnzhU6nU7s2LFDlJeXS4+rV69KMXPmzBH9+/cX27ZtE/v37xdGo1EYjUbpfH19vYiOjhYTJkwQhYWFIisrS/Tt21csXrxYivnhhx9EYGCgWLBggTh69KhYuXKlUKlUIisrS4pZv3690Gg0IjMzUxQXF4vZs2eL4OBgh9lobZWlLZw1RkRE1PW4cv92KREC0Oxj7dq1Usy1a9fE008/LXr37i0CAwPFgw8+KMrLyx2uc+rUKfHAAw+IgIAAERoaKp577jlRV1fnELN9+3YRExMj1Gq1GDx4sMN72K1YsUL0799fqNVqERcXJ/bu3etw3pmytIaJEBERUdfjyv1bIYQQvmqN6uwsFgt0Oh3MZjO0Wq2vi0NEREROcOX+zb3GiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2erh6wIQERHJndUmkF96EWcvVyMsyB9xg0KgUip8XSxZYCJERETkQ1lF5Vi6qRjl5mrpWITOH+mTDZgYHeHDkskDu8aIiIh8JKuoHHM/OOCQBAGAyVyNuR8cQFZRuY9KJh9MhIiIyGlWm0DeyQv4svAn5J28AKtN+LpIXZbVJrB0UzGaq0H7saWbilnHHsauMSIicgq7cNwrv/Rik5agxgSAcnM18ksvwjikj/cKJjNsESIiojaxC8f9zl5uOQlqTxy1DxMhIiJqFbtwPCMsyN+tcdQ+TISIiKhVrnThkPPiBoUgQuePlibJK9DQ9Rg3KMSbxZIdJkLdGAc1EpE7sAvHM1RKBdInGwCgSTJkf54+2cD1hDyMg6W7KQ5qJCJ3YReO50yMjsCqx0Y1+b3W8/faa5gIdUP2QY3Xt//YBzWuemwU/+ciIqfZu3BM5upmxwkp0HDjZhdO+0yMjsD9Bj1XlvYRdo11MxzUSETuxi4cz1MpFTAO6YMpMTfAOKQP69KLXE6Edu3ahcmTJyMyMhIKhQJffPGFw/nf/e53UCgUDo+JEyc6xFy8eBGPPvootFotgoOD8eSTT+LKlSsOMYcOHcLdd98Nf39/REVFYdmyZU3KsnHjRgwfPhz+/v4YMWIEtmzZ4nBeCIG0tDREREQgICAACQkJOH78uKsfuUvhoEYi8gR7F45e59j9pdf5s5WZujSXu8aqqqowcuRIPPHEE3jooYeajZk4cSLWrl0rPddoNA7nH330UZSXlyMnJwd1dXV4/PHHMXv2bHz00UcAAIvFggkTJiAhIQGrV6/G4cOH8cQTTyA4OBizZ88GAOzZswcPP/wwMjIy8Itf/AIfffQRpk6digMHDiA6OhoAsGzZMrz99ttYt24dBg0ahD/96U9ITExEcXEx/P27Z182BzUSkaewC4e6I4UQot19JAqFAp9//jmmTp0qHfvd736HysrKJi1FdkePHoXBYMD333+P22+/HQCQlZWFSZMm4fTp04iMjMSqVavwxz/+ESaTCWq1GgCwaNEifPHFFzh27BgAYPr06aiqqsLmzZula48ePRoxMTFYvXo1hBCIjIzEc889h+effx4AYDabER4ejszMTMyYMaPNz2exWKDT6WA2m6HVattTRV6Xd/ICHl6zt824j2eN5kql1C1w124iup4r92+PjBHasWMHwsLCMGzYMMydOxcXLlyQzuXl5SE4OFhKggAgISEBSqUS+/btk2LGjh0rJUEAkJiYiJKSEly6dEmKSUhIcHjfxMRE5OXlAQBKS0thMpkcYnQ6HeLj46WY69XU1MBisTg8uhquS0FyklVUjjGvb8PDa/bi2fWFeHjNXox5fRtXOSYip7k9EZo4cSL+8Y9/IDc3F6+//jp27tyJBx54AFarFQBgMpkQFhbm8JoePXogJCQEJpNJigkPD3eIsT9vK6bx+cavay7mehkZGdDpdNIjKirK5c/vaxzUSHLBLR/kgeuhkae5ffp84y6nESNG4NZbb8WQIUOwY8cOjB8/3t1v51aLFy9Gamqq9NxisXTJZIjrUlB319bsSAUaZkfeb9Az6e/CuB4aeYPH1xEaPHgwQkNDceLECYwfPx56vR5nz551iKmvr8fFixeh1+sBAHq9HhUVFQ4x9udtxTQ+bz8WERHhEBMTE9NsWTUaTZOB3V0VBzVSd8Zdu7s/rodG3uLxdYROnz6NCxcuSMmI0WhEZWUlCgoKpJht27bBZrMhPj5eitm1axfq6uqkmJycHAwbNgy9e/eWYnJzcx3eKycnB0ajEQAwaNAg6PV6hxiLxYJ9+/ZJMd0d16Wg7oqzI7s3rodG3uRyInTlyhUUFhaisLAQQMOg5MLCQpSVleHKlStYsGAB9u7di1OnTiE3NxdTpkzB0KFDkZiYCAC4+eabMXHiRMyaNQv5+fnYvXs3UlJSMGPGDERGRgIAHnnkEajVajz55JM4cuQINmzYgOXLlzt0Wz377LPIysrCX/7yFxw7dgxLlizB/v37kZKSAqBhRtu8efPwyiuv4KuvvsLhw4fx29/+FpGRkQ6z3Iio6+GWD90b10Mjb3K5a2z//v0YN26c9NyenMycOROrVq3CoUOHsG7dOlRWViIyMhITJkzAyy+/7NDl9OGHHyIlJQXjx4+HUqnEtGnT8Pbbb0vndTodtm7diuTkZMTGxiI0NBRpaWnSGkIAcOedd+Kjjz7Ciy++iD/84Q+48cYb8cUXX0hrCAHACy+8gKqqKsyePRuVlZUYM2YMsrKyuu0aQkRywS0fuje2+JE3dWgdoe6uK64jRCQX9jEkABySIXsHMMeQdF1cD406yufrCBEReRq3fOi+uB4aeRN3nyeiLouzI7sn+3pocz84AAWab/HjemjkLuwaawW7xoiIfIfrCFF7uXL/ZosQERF1SmzxI29gIkRERJ2WfT00Ik/hYGkiIiKSLSZCREREJFvsGiMi6iSsNsHxMERexkSIiKgT4AwpIt9g1xgRkY/ZV8m+fn8t+07rWUXlPioZUffHRIiIyIe40zqRbzERIiLyIe60TuRbTISIiHyIO60T+RYTISIiHwoL8m87yIU4InINZ40RkU9wqngD+07rJnN1s+OEFAD03GmdyGOYCBGR13Gq+M+40zqRb7FrjIi8ilPFm5oYHYFVj42CXufY/aXX+WPVY6NklxwSeRNbhIjIK6w2gb0/XMCi/zvc4lRxBRqmit9v0MuuBYQ7rRP5BhMhIvK45rrCmtN4qrgcdxznTutE3sdEiIg8yt4V5spygJwqTkTewjFCROQxra2a3BpOFScib2GLkBOu1tajR219k+NKhQL+fiqHuJZ0JPZarRWihVuJAgoEqNsXW11nhU20fIsKVPfweWyAnwoKRcMYiZp6a6vbDLgS699DBeV/x17U1ttQb7O5JVbTQyWN6XAlts5qQ5215Vi1SokeKqXLsfVWG2pbifVTKeHXjlirTaCm3tpibA+lEuoeyjZXTW5OuFaD6Bu0zf4/Yr8uANhsAtWtlEGlVEDTo+H7LoTAtTr3xHrr/3v+RjgX667fCKtNYP+pSzh3pRp9e/ljzNBQ+P33u8bfCM/9Rrga29b/941jncVEyAlx/5MLpSawyfFxw/pi7eNx0vPYl79t8Qc0flAINvzeKD0f8/p2XKyqbTb21n46fJUyRnqe8L878VPltWZjbwzrhZzUe6Tnv3znOxw/e6XZ2BuCA7B70X3S81//NQ+HTpubjQ3pqcaBP90vPZ/593zsa2GJ/wA/FY6+PFF6PveDAmwvOddsLACcei1J+u/UTwqx5bCpxdjilxKlH8U/fFaE/ztwusXYghcT0KeXBgDwyuaj+Ofe/7QY+/+9MA5RIQ1/0ze2luC9XT+0GLt1/ljcFB4EAFi5/QSW5x5vMfbL5LswMioYALB2dykyvjnWYuzHs0ZL40E+zi9D2pdHWoz9++9ux33DwwEAXxz8CQs+PdRi7MpHRiHp1oZZRtlHKpD80YEWY//8q1vx/9weBQDYdfwcnsjc32LsS1NuwW+NAwE0bAvx8Jq9LcYufmA4fn/PEJe6uOxTxyssNRixZGuzMbPHDsYfJt0MAPip8hruXra9xev9ZvQAvDw1GgBwsaoWsa9822LstFH98JdfjwQAXKuzwpCW3WLspBF6vPtorPS8tVj+RjToir8RfYPUeHlKNCZGR/A3woO/EQBQ9JMZU1bubjH22fE3Yv79NwEATpy7gglv7moxtvFvhLPYNUZEHuNKF5de54+0X7j2A0bkKecu18p2OQe5UQjRSpujzFksFuh0OpSfuwCtVtvkPJu9PR/LrrEGXbXZ22oTuOu1XJgsNS3G6gJ64N1HYjF6SB8oAKebvdk1xt8IoGO/EbX1NiT8705UtPD9tK/qve25e1usM4C/Ec3F+rprzH7/NpvNzd6/G2Mi1ApXKpKImmefNQY0v2oyFwwkX8k7eaHV7hu7xl1U1DW4cv9m1xgReRRXTabOytkxbFzOoXvjYGki8jiumkydkbNj2LicQ/fGRIiIvIKrJlNnEzcoBBE6f5jM1c2OALKPEYobFOLtopEXsWuMiIhkSaVUIH2yAcDPY9bs7M/TJxvYctnNMREiIiLZ4hg2YtcYERHJGsewyRsTISIikj2OYZMvdo0RERGRbLFFqAuw2gSbbImIiDyAiVAnl1VUjqWbih128I7Q+SN9soGD+IiIiDqIXWOdmH1rgsZJEACYzNXcDJCIiMgNmAh1UlabwNJNxc0u8mU/tnRTcasbDBIREVHrXE6Edu3ahcmTJyMyMhIKhQJffPGFw3khBNLS0hAREYGAgAAkJCTg+PHjDjEXL17Eo48+Cq1Wi+DgYDz55JO4cuWKQ8yhQ4dw9913w9/fH1FRUVi2bFmTsmzcuBHDhw+Hv78/RowYgS1btrhcls4qv/Rik5agxgSAcnM18ksveq9QRERE3YzLiVBVVRVGjhyJlStXNnt+2bJlePvtt7F69Wrs27cPPXv2RGJiIqqrf76pP/roozhy5AhycnKwefNm7Nq1C7Nnz5bOWywWTJgwAQMGDEBBQQH+/Oc/Y8mSJXjvvfekmD179uDhhx/Gk08+iYMHD2Lq1KmYOnUqioqKXCpLZ8XNAImIiDxPIYRod9+KQqHA559/jqlTpwJoaIGJjIzEc889h+effx4AYDabER4ejszMTMyYMQNHjx6FwWDA999/j9tvvx0AkJWVhUmTJuH06dOIjIzEqlWr8Mc//hEmkwlqtRoAsGjRInzxxRc4duwYAGD69OmoqqrC5s2bpfKMHj0aMTExWL16tVNlaYvFYoFOp4PZbIZWq21vNbVL3skLeHjN3jbjPp41mmtfEBERNeLK/dutY4RKS0thMpmQkJAgHdPpdIiPj0deXh4AIC8vD8HBwVISBAAJCQlQKpXYt2+fFDN27FgpCQKAxMRElJSU4NKlS1JM4/exx9jfx5myXK+mpgYWi8Xh4Sv2zQBbmiSvQMPsMW4GSERE1H5uTYRMJhMAIDw83OF4eHi4dM5kMiEsLMzhfI8ePRASEuIQ09w1Gr9HSzGNz7dVlutlZGRAp9NJj6ioKCc+tWdwM0AiIiLP46yxRhYvXgyz2Sw9fvzxR5+Wh5sBeobVJpB38gK+LPwJeScvcOYdEZGMuXVBRb1eDwCoqKhARMTPN+mKigrExMRIMWfPnnV4XX19PS5evCi9Xq/Xo6KiwiHG/rytmMbn2yrL9TQaDTQajdOf1xu4GaB7cYFKIiJqzK0tQoMGDYJer0dubq50zGKxYN++fTAajQAAo9GIyspKFBQUSDHbtm2DzWZDfHy8FLNr1y7U1dVJMTk5ORg2bBh69+4txTR+H3uM/X2cKUtXYd8McErMDTAO6cMkqJ1aWqCy3FyNOR8cwJZDZ3xUMiIi8hWXE6ErV66gsLAQhYWFABoGJRcWFqKsrAwKhQLz5s3DK6+8gq+++gqHDx/Gb3/7W0RGRkozy26++WZMnDgRs2bNQn5+Pnbv3o2UlBTMmDEDkZGRAIBHHnkEarUaTz75JI4cOYINGzZg+fLlSE1Nlcrx7LPPIisrC3/5y19w7NgxLFmyBPv370dKSgoAOFUWko/WFqi0S/n4ILYc4mrdRERy4vL0+R07dmDcuHFNjs+cOROZmZkQQiA9PR3vvfceKisrMWbMGLz77ru46aabpNiLFy8iJSUFmzZtglKpxLRp0/D222+jV69eUsyhQ4eQnJyM77//HqGhoXjmmWewcOFCh/fcuHEjXnzxRZw6dQo33ngjli1bhkmTJknnnSlLa3w5fZ7cy9nlCABgNcdfERF1aa7cvzu0jlB3x0So+/iy8Cc8u77QqdgInT++W3gfuyCJiLoon60jRL7F2VAtCwvybzvov7h1CRGRfLh11hj5DmdDtc6+QGVr+7c1xq1LiIjkgS1C3UBLs6FM5mrM/eAAsoo4ALjxApXOcKUFiYiIui4mQp2YM11drc2Gsh9buqmY3WRoWJPp3UduQ2tDf7h1CRGRvLBrrJNytqsrv/Riq909Aj+PeeHmrMCkWyPxDhR4+qMDTc5x6xIiIvlhi1An5EpXl7NjWTjm5WeTbo3A6sdGIaILbl3CAfFERO7FFqFOpq2uLgUaurruN+ihUiqcHsvCMS+OuuLWJRwQT0TkfmwR6mRc6eoCfp4N1dLtm2NeWtaVti7hgHgiIs9gItTJuNrV1Xg21PW3cY556R44IJ6IyHOYCHUy7enqmhgdgVWPjYK+C455oba52kpIRETO4xihTsbe1WUyVzfbAqBAQ4JzfVdXVxzzQs7hgHgiIs9hItTJ2Lu65n5wAArAIRlqq6vLPuaFuhcOiCci8hx2jXVC7OqixjggnojIc9gi1Emxq4vsOtJKSERErVMIITjVpAUWiwU6nQ5msxlardbXxSGZ4zpCRETOceX+zRYhoi6CrYRERO7HRIioC+GAeCIi92IiRCQDVptgSxIRUTOYCBF1cxxbRETUMk6f70S4szi5G/coIyJqHVuEOgn+q53cra09yhRo2KPsfoOe3WREJFtsEeoE+K/2jmFLWvO4RxkRUdvYIuRj/Fd7x7AlrWXco4yIqG1sEfIx/qu9/diS1jruUUZE1DYmQj7Gf7W3T1staUBDS5qcu8m4RxkRUduYCPkY/9XePmxJa5t9jzIATZIh7lFGRNSAiZCP8V/tzWtrADRb0tpmtQnoAtR4/K6B6N1T7XBOr/PHqsdGyX4cFRERB0v7GHcWb8qZAdBsSWtdc3UY0tMPD8bcgASDnitLExH9F1uEOoGJ0RFY9dgo6HWON205/qvd2QHQbElrWUt1eKmqDn/ffQrma7VMgoiI/ostQp0EdxZ3fSkBtqQ1xeUYiIhcwxahTsS+s/iUmBtgHNJHdjcqVwdAsyWtKQ4iJyJyDVuEnHC1th5BQkChaEhMauttqLfZoFIqoOmhcogDAP8eKij/m8TUWW2os9qgVCjg79e+2Gu1VggIaHqopOSo3mpDbQdjq+ussAkBtUqJHqqGnNhqE6iptzaJraqpR37pRVy6WosIXYDU5VRTb4UCCgSom17XT6WE33XXbS3W2YHNZyqvAugDAJhg0OOuoaHYf+oSLNV1Uktavc2Gq7X16KFUQt2joQxCCFyrswIAAvxUTf6ersQ687d3x/ekub9na7E/Xqpyqg5PX7qKq7W6Dn9PWvp7uhLb+HtiswlU1zfUe6C6R7tia+qtsNpEi39PV2Jd+du393vSXX4jOvq3d8f3pLm/Z3u/J931N8LVv31X/o1wFluEnBD3P7m4WFUrPX9v10kY0rKR/uURh7jYl7+FIS0bP1Vek479I+8/MKRlY+H/HXKIHfP6dhjSsnHi3BXp2KcFp2FIy8YzHx90iE34350wpGWj6CezdGzzoXIY0rLx1Lr9DrG/fOc7GNKyHf7Fn3vsLAxp2Xj0/X0Osb/+ax4MadnYdfycdGzPyfMwpGXjwXf3SMeyispx20tb8Xjm90j95F94eM1ejHl9G1btOAFDWjYeWL7L4bpzPyiAIS0bXxz8STp2zGSBIS0b976x3SE29ZNCGNKy8XF+mdMDmwv+Uyn999nLNRixZCtm/WO/Q0vaK5uPwpCWjZXbT0ixlup6GNKyYUjLRn2jWWhvbC2BIS0bb2wtkY7V24QUa6mul46v3N7wmV/ZfNShTLcu2QpDWjbOXq6Rjq3dXQpDWjb+8FmRQ2z8q7kwpGXjPxevSsc+zi+DIS0bqZ8UOsTe+0bD9+SYySId++LgTzCkZWPuBwUOsQ8s34UXPj3cWtVJ3tnW8Dn2nDwvHdt1/BwMadn49V/zHGIffX8fDGnZyD12VjqWX3oRhrRs/PKd7xxin1q3H4a0bGw+9PNilkU/mWFIy0bC/+50iH3m44MwpGXj04LT0rET567AkJaNMa87fk8W/t8hGNKy8Y+8/0jHfqq8BkNaNmJf/tYhNv3LIzCkZeO9XSelYxeraqW/Z2OvfXMMhrRsLM/9t3TsWp1VirXf6ABgee6/YUjLxmvfHHO4hj1Wzr8RADDz7/kwpGUj+0iFdOxg2SW3/kbY/efiVRjSshH/aq5D7B8+K4IhLRtrd5dKx85eroEhLRu3LtnqECvX3whDWjYOll2SjmUfqYAhLRsz/57vEPvgu3u6/G+Es5gIUavsA29rrY6jTkzmaryx9d8tvKp97AOg2xIVEujW9+1u+vRUtzmIPFCjaiHCOTbR8H24XF3P/d2IqEtTCCH4C9YCi8UCnU6H8nMXEN6nt0+bvettNhz60YzzVTUIC/LHqP7BsArh0WZvP5USY17f1uqYk3CtBrmp96KXf+tNlM42fWYVlWPOBweavI99QPTyGSPxQHQkm71bid1x7CySP2poMWhuEPmqx0bh3mFh7W72zioqx5KvjsBk+flftvblDezXZdcYu8Yax7JrrHP9Rsiha8x+/zabzdBqtWgNE6FWuFKRnuSrjUXzTl7Aw2v2thn38azRMA7p47b35UaqHeepOrS3EF7/o9E4yeLfiIh8zZX7NwdLd3It3Xjs6+p48sbjq9WbuZRAx3miDjk1n4i6IyZCnZivbzy+XL3ZvpQAtZ+769CVqfn82xFRV8HB0p2Yr9eE4erN1Bj3dyOi7sjtidCSJUugUCgcHsOHD5fOV1dXIzk5GX369EGvXr0wbdo0VFRUOFyjrKwMSUlJCAwMRFhYGBYsWID6+nqHmB07dmDUqFHQaDQYOnQoMjMzm5Rl5cqVGDhwIPz9/REfH4/8/PwmMZ2Zr2883L2cGm9+e77RtN/WyHV/NyLqmjzSNXbLLbfg229/XtujR4+f32b+/Pn4+uuvsXHjRuh0OqSkpOChhx7C7t27AQBWqxVJSUnQ6/XYs2cPysvL8dvf/hZ+fn549dVXAQClpaVISkrCnDlz8OGHHyI3NxdPPfUUIiIikJiYCADYsGEDUlNTsXr1asTHx+Ott95CYmIiSkpKEBYW5omP7XadYWNR++rN1w+81XPwcrfX3IBrpQJoaaa8Ag3fC7YQElFX4vZZY0uWLMEXX3yBwsLCJufMZjP69u2Ljz76CL/61a8AAMeOHcPNN9+MvLw8jB49Gt988w1+8Ytf4MyZMwgPDwcArF69GgsXLsS5c+egVquxcOFCfP311ygq+nkRqhkzZqCyshJZWVkAgPj4eNxxxx145513AAA2mw1RUVF45plnsGjRIqc+izdnjVltosnAVgAY8/o2mMzVzY4Tst94vlt4n8dbZZorH1uCuq+WBum3hLPGiKgzceX+7ZExQsePH0dkZCQGDx6MRx99FGVlDSuCFhQUoK6uDgkJCVLs8OHD0b9/f+TlNaxUmZeXhxEjRkhJEAAkJibCYrHgyJEjUkzja9hj7Neora1FQUGBQ4xSqURCQoIU05yamhpYLBaHhzdkFZVjzOvb8PCavXh2faG0cnNOscmlrqnG3RjuXuRO7vugyUlrg/Ttrv/zy3l/NyLq2tzeNRYfH4/MzEwMGzYM5eXlWLp0Ke6++24UFRXBZDJBrVYjODjY4TXh4eEwmUwAAJPJ5JAE2c/bz7UWY7FYcO3aNVy6dAlWq7XZmGPHHJfHbywjIwNLly5t1+duD6tN4J1tx/Hmt8ebnGs8Pd6ZrimuvUPu0tYgfaChe+xPSTcjNEjDFkIi6tLcngg98MAD0n/feuutiI+Px4ABA/DJJ58gICDA3W/nVosXL0Zqaqr03GKxICoqyiPv1bA6bzFMluZvOI2nx3+38L5W14Tx5VpD1P04O/g+NEiDKTE3eLg0RESe5fHp88HBwbjppptw4sQJ6PV61NbWorKy0iGmoqICer0eAKDX65vMIrM/bytGq9UiICAAoaGhUKlUzcbYr9EcjUYDrVbr8PAEe+LSUhJk13h6fEtdU22tNQQ0JFPcC4qc1RkG6RMReYvHE6ErV67g5MmTiIiIQGxsLPz8/JCb+/OOwSUlJSgrK4PRaAQAGI1GHD58GGfP/ryLbU5ODrRaLQwGgxTT+Br2GPs11Go1YmNjHWJsNhtyc3OlGF9xZvzF9Vr7F7qv1xqi7ofrRxGRnLg9EXr++eexc+dOnDp1Cnv27MGDDz4IlUqFhx9+GDqdDk8++SRSU1Oxfft2FBQU4PHHH4fRaMTo0aMBABMmTIDBYMBvfvMb/Otf/0J2djZefPFFJCcnQ6PRAADmzJmDH374AS+88AKOHTuGd999F5988gnmz58vlSM1NRVr1qzBunXrcPToUcydOxdVVVV4/PHH3f2RXeLM+IvrtfYvb2e7Mb4tNrn0niRfXD+KiOTE7WOETp8+jYcffhgXLlxA3759MWbMGOzduxd9+/YFALz55ptQKpWYNm0aampqkJiYiHfffVd6vUqlwubNmzF37lwYjUb07NkTM2fOxEsvvSTFDBo0CF9//TXmz5+P5cuXo1+/fnj//felNYQAYPr06Th37hzS0tJgMpkQExODrKysJgOova09ix9eqqpt8Zyz3RN/230KdwwK4VghcgrXjyIiueDu863wxDpCzu7o3lhEK2sFWW2i1bWGnL0OUXO4fhQRdUU+X0eIWmYff+GK1sb42LsxnMlmOVaIXMX1o4iou2Mi5GUqpQK/HOl6t0JrXWoToyPwxF0DO3wdIiIiuWEi5GVWm8BX/yp3+XWnzle1ev5+Q8vLAjS2/9RFfH7Q/StPExERdUUe2XSVWtaeWWMA8HF+GVLuu7HFrgl7l1tbY4X+ubcM/9zbsOUJV552D46jISLqupgIeVl7u6ZMlhrkl16EcUifZs/bxwrN/eAAFIDTY4a48nTHcGsTIqKujV1jXtaR1XjbSqLsU571Lg7G5srT7WNfIfz6Fj771iZZRa53gRIRkXcxEfKyuEEhCPBrX7U7k0RNjI7Adwvvw5+Sbnbqmlx5un24tQkRUffARMjLVEoFpt/u+kauETp/xA7ojbyTF/BlYeuDnVVKBUKDNC5dn7PJXMOtTYiIugeOEfKB3j1dS1IA4JcjI3DPn7c7PRbF1S44bqDpGmcTRyaYRESdG1uEvMxqE3hv10mXXjPiBi3e21Xq9FgUq03AZhMIDvBr89rcQLN9uEM7EVH3wBYhL9tz4jyqaq0uvebwT5Zmjws0JDJLNxXjfoMeKqWi2VlMbensG2h2xunpbS1XoEDDvlxMMImIOjcmQl720qZit16v8VgU87VazP3ggFNT54GuMc27s05Pb225Au7QTkTUdTAR8qIth8px/NwVj1zbZKnG698cazUJCvRTYsItetzQOwB3DgnF6MG+3TuqrZYe+/T06z+TqZOsf8Qd2omIuj7uPt8Kd+4+b7UJjFy6FVdq6t1UOke/GnUDPj3wk9Pxvm5Vaaulx2oTGPP6tha7+OxdT98tvM/nrS6dseuOiEjOXLl/s0XIS/b+cMEjSZACQHCgn0tJENDQnTbngwN495HbMOnWSLeXqzn2hOHbYhP+tvtUk/ONW3p0AWqnp6e3tNq2t9h3aCcioq6HiZCX5J284PZr2tscOtKkl/LxQbwDBSbd6tmWIWcGcTce/P3CxOFOXZfT04mIqCM4fd5r3N8Dqdf5Y17CTai8Wtfua9gE8PRHnt0OoqWtKJpjb+m5eKXGqWtzejoREXUEEyEvMQ4Oddu1nrxrID6eNRrfLbwPA0MD3XJNT20H0dpWFK0J6alGhM4fLY204fpHRETkDkyEvGS0G8eQfF74E2IH9IZKqXBbi0hHtoOw2kSLW3+0tRVFS/S6AKRPNgBAk2SI09OJiMhdOEbIS1RKBQL9lLhaZ+vwtS5W1WF0Ri5efTAato5fTtKe8TZtzf5y9ZqNFyJUKRWcnk5ERB7FRMiL+vT0w9VK58a+tOViVcPiibrAtrfRcJarrUvOrPPjyjWba+mZGB2B+w16Tk8nIiKPYCLkRWcvuycJshNAhwZKN9anp9ql8Tatjf1pPPtr54JxrW5F0VhLLT2cnk5ERJ7CRMhLauttqHFtizGvGhmla7WV5fpFA21COLXOT8F/LrW6FYUA8MRdA3G/Qc+WHiIi8jomQl7yz7xTvi5Cq/71oxlWm2g2EWluHJAzO9sDDeOOpsTcwLE+RETUKTER8pITHtpjzF0uVNU2u0pzS+OAKq851yVnHyPEsT5ERNQZMRHykoJT7Zua3hr79hqX3DRO6OzlaocusNBeGiz56ki7loJsPPvLjmN9iIios2Ei5CWe2NtWAMh4aARWbj+Bwz9ZOny9U+erWt3o1Flc54eIiLoKJkJe0lOjcvs1ewf6oabe5pYkSKEA3vz2eLteG6hW4WrtzyPBOfaHiIi6CiZCXnLlWq3br3npah2eXV/olmt1pMHK30+FNb+5Heeralod+3P9zDOOESIiIl9jIuQlJy+4dw2hzuRiVS2USgWmxNzQYkxbK1ATERH5Avca8xL3jxDqXHafON/ipq0t7T5vX4E6q8hzO98TERG1hokQucU7208g9uUcLP/2uENC1NYK1EDDCtQtJVFERESexESI3KbyWh3e/PbfiH0lR2rlaWv3efsK1Pml7l9egIiIqC1MhMjtKq/WYc5/u7yc3X3e1V3qiYiI3IGJEDktpKdrO90v3VSM0F4ap2Jd2aWeiIjIXZgIkdP+9ItbkDJuiNPx5eZqQDTMDmtpkrwCDecbr0BNRETkLUyEyGl6rT/uGtrXpdecr6pB+mQDADRJhrgCNRER+RoTIXKKvdUmblAIInTOd2OFBfljYnQEVj02CvrrXqfX+WPVY6O4jhAREfmMLBZUXLlyJf785z/DZDJh5MiRWLFiBeLi4nxdrE4jZdwQnLpwFZsPNb+ejwKOrTbpkw3N7kh//Wsab7rK3eeJiKgz6vYtQhs2bEBqairS09Nx4MABjBw5EomJiTh79qyvi9Zp3BgehHceGYV3HxmFkJ5qh3MRzbTa2Ft4ggObHzzdUpeXfff5KTE3wDikD5MgIiLyOYXwxLbonUh8fDzuuOMOvPPOOwAAm82GqKgoPPPMM1i0aFGrr7VYLNDpdDCbzdBqtR0qx8BFX3fo9Z708azRMA7pA8C1/cCsNoF3th3H2t2nUHmtTjrOrTOIiMiXXLl/d+uusdraWhQUFGDx4sXSMaVSiYSEBOTl5fmwZJ3D9d1XwM+tNs5QKRV4NuEmpNx3I7u8iIioS+rWidD58+dhtVoRHh7ucDw8PBzHjh1rEl9TU4Oamp83R7VYLB4vo6+4c8aWK8kTERFRZ9Ltxwi5IiMjAzqdTnpERUX5ukhO66lWtXr++vE8nLFFRETUzVuEQkNDoVKpUFFR4XC8oqICer2+SfzixYuRmpoqPbdYLJ0+GfJTKZAybihS7rsROcUmLPnqCEyWn1u19FoNlvzyFs7YIiIiaoYsBkvHxcVhxYoVABoGS/fv3x8pKSleHSwNuHfAdE+NCk+NGYz/d/yNDgmNK4OdiYiIuiMOlm4kNTUVM2fOxO233464uDi89dZbqKqqwuOPP+71spx6LaldyZBKCUwZeQMejLkBF6/VtprgcLwOERGR87p9IjR9+nScO3cOaWlpMJlMiImJQVZWVpMB1N7SUjLkr2roxqoTgM0m0Evjh5sjdfh/YvvhzqGhbNUhIiLygG7fNdYR7u4aIyIiIs9z5f7NWWNEREQkW0yEiIiISLaYCBEREZFsMREiIiIi2WIiRERERLLFRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW91+i42OsC+6bbFYfFwSIiIicpb9vu3M5hlMhFpx+fJlAEBUVJSPS0JERESuunz5MnQ6Xasx3GusFTabDWfOnEFQUBAUCvduemqxWBAVFYUff/xRtvuYsQ4asB5YB3ashwasB9aBXXvrQQiBy5cvIzIyEkpl66OA2CLUCqVSiX79+nn0PbRaray/5ADrwI71wDqwYz00YD2wDuzaUw9ttQTZcbA0ERERyRYTISIiIpItJkI+otFokJ6eDo1G4+ui+AzroAHrgXVgx3powHpgHdh5ox44WJqIiIhkiy1CREREJFtMhIiIiEi2mAgRERGRbDERIiIiItliIuRBK1euxMCBA+Hv74/4+Hjk5+e3Gr9x40YMHz4c/v7+GDFiBLZs2eKlknqOK3Vw5MgRTJs2DQMHDoRCocBbb73lvYJ6mCv1sGbNGtx9993o3bs3evfujYSEhDa/O12BK3Xw2Wef4fbbb0dwcDB69uyJmJgY/POf//RiaT3H1d8Fu/Xr10OhUGDq1KmeLaAXuFIHmZmZUCgUDg9/f38vltZzXP0uVFZWIjk5GREREdBoNLjppptkd5+49957m3wfFAoFkpKS2l8AQR6xfv16oVarxd///ndx5MgRMWvWLBEcHCwqKiqajd+9e7dQqVRi2bJlori4WLz44ovCz89PHD582Msldx9X6yA/P188//zz4uOPPxZ6vV68+eab3i2wh7haD4888ohYuXKlOHjwoDh69Kj43e9+J3Q6nTh9+rSXS+4+rtbB9u3bxWeffSaKi4vFiRMnxFtvvSVUKpXIysrycsndy9V6sCstLRU33HCDuPvuu8WUKVO8U1gPcbUO1q5dK7RarSgvL5ceJpPJy6V2P1froaamRtx+++1i0qRJ4rvvvhOlpaVix44dorCw0Msldy9X6+HChQsO34WioiKhUqnE2rVr210GJkIeEhcXJ5KTk6XnVqtVREZGioyMjGbjf/3rX4ukpCSHY/Hx8eL3v/+9R8vpSa7WQWMDBgzoNolQR+pBCCHq6+tFUFCQWLdunaeK6HEdrQMhhLjtttvEiy++6InieU176qG+vl7ceeed4v333xczZ87s8omQq3Wwdu1aodPpvFQ673G1HlatWiUGDx4samtrvVVEr+job8Obb74pgoKCxJUrV9pdBnaNeUBtbS0KCgqQkJAgHVMqlUhISEBeXl6zr8nLy3OIB4DExMQW4zu79tRBd+SOerh69Srq6uoQEhLiqWJ6VEfrQAiB3NxclJSUYOzYsZ4sqke1tx5eeuklhIWF4cknn/RGMT2qvXVw5coVDBgwAFFRUZgyZQqOHDnijeJ6THvq4auvvoLRaERycjLCw8MRHR2NV199FVar1VvFdjt3/D7+7W9/w4wZM9CzZ892l4OJkAecP38eVqsV4eHhDsfDw8NhMpmafY3JZHIpvrNrTx10R+6oh4ULFyIyMrJJotxVtLcOzGYzevXqBbVajaSkJKxYsQL333+/p4vrMe2ph++++w5/+9vfsGbNGm8U0ePaUwfDhg3D3//+d3z55Zf44IMPYLPZcOedd+L06dPeKLJHtKcefvjhB3z66aewWq3YsmUL/vSnP+Evf/kLXnnlFW8U2SM6+vuYn5+PoqIiPPXUUx0qB3efJ+rEXnvtNaxfvx47duzoNgNEnRUUFITCwkJcuXIFubm5SE1NxeDBg3Hvvff6umhecfnyZfzmN7/BmjVrEBoa6uvi+IzRaITRaJSe33nnnbj55pvx17/+FS+//LIPS+ZdNpsNYWFheO+996BSqRAbG4uffvoJf/7zn5Genu7r4vnE3/72N4wYMQJxcXEdug4TIQ8IDQ2FSqVCRUWFw/GKigro9fpmX6PX612K7+zaUwfdUUfq4Y033sBrr72Gb7/9Frfeeqsni+lR7a0DpVKJoUOHAgBiYmJw9OhRZGRkdNlEyNV6OHnyJE6dOoXJkydLx2w2GwCgR48eKCkpwZAhQzxbaDdzx++Cn58fbrvtNpw4ccITRfSK9tRDREQE/Pz8oFKppGM333wzTCYTamtroVarPVpmT+jI96Gqqgrr16/HSy+91OFysGvMA9RqNWJjY5Gbmysds9lsyM3NdfiXTWNGo9EhHgBycnJajO/s2lMH3VF762HZsmV4+eWXkZWVhdtvv90bRfUYd30XbDYbampqPFFEr3C1HoYPH47Dhw+jsLBQevzyl7/EuHHjUFhYiKioKG8W3y3c8V2wWq04fPgwIiIiPFVMj2tPPdx11104ceKElAwDwL///W9ERER0ySQI6Nj3YePGjaipqcFjjz3W8YK0e5g1tWr9+vVCo9GIzMxMUVxcLGbPni2Cg4OlaZ+/+c1vxKJFi6T43bt3ix49eog33nhDHD16VKSnp3eL6fOu1EFNTY04ePCgOHjwoIiIiBDPP/+8OHjwoDh+/LivPoJbuFoPr732mlCr1eLTTz91mCZ6+fJlX32EDnO1Dl599VWxdetWcfLkSVFcXCzeeOMN0aNHD7FmzRpffQS3cLUertcdZo25WgdLly4V2dnZ4uTJk6KgoEDMmDFD+Pv7iyNHjvjqI7iFq/VQVlYmgoKCREpKiigpKRGbN28WYWFh4pVXXvHVR3CL9v4/MWbMGDF9+nS3lIGJkAetWLFC9O/fX6jVahEXFyf27t0rnbvnnnvEzJkzHeI/+eQTcdNNNwm1Wi1uueUW8fXXX3u5xO7nSh2UlpYKAE0e99xzj/cL7mau1MOAAQOarYf09HTvF9yNXKmDP/7xj2Lo0KHC399f9O7dWxiNRrF+/XoflNr9XP1daKw7JEJCuFYH8+bNk2LDw8PFpEmTxIEDB3xQavdz9buwZ88eER8fLzQajRg8eLD4n//5H1FfX+/lUrufq/Vw7NgxAUBs3brVLe+vEEKIjrcrEREREXU9HCNEREREssVEiIiIiGSLiRARERHJFhMhIiIiki0mQkRERCRbTISIiIhItpgIERERkWwxESIiIiLZYiJEREREssVEiIiIiGSLiRARERHJFhMhIiIikq3/HyoM9DADWh7BAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 9 COIs with pop > 1.05 * 119186.34343434343\n",
      "rebuilding COIgeom from bgs for COI 0\n",
      "rebuilding COIgeom from bgs for COI 100\n",
      "rebuilding COIgeom from bgs for COI 200\n",
      "rebuilding COIgeom from bgs for COI 300\n",
      "rebuilding COIgeom from bgs for COI 400\n",
      "rebuilding COIgeom from bgs for COI 500\n",
      "rebuilding COIgeom from bgs for COI 600\n",
      "rebuilding COIgeom from bgs for COI 700\n",
      "rebuilding COIgeom from bgs for COI 800\n",
      "rebuilding COIgeom from bgs for COI 900\n",
      "rebuilding COIgeom from bgs for COI 1000\n",
      "rebuilding COIgeom from bgs for COI 1100\n",
      "rebuilding COIgeom from bgs for COI 1200\n",
      "rebuilding COIgeom from bgs for COI 1300\n",
      "rebuilding COIgeom from bgs for COI 1400\n",
      "rebuilding COIgeom from bgs for COI 1500\n",
      "rebuilding COIgeom from bgs for COI 1600\n",
      "rebuilding COIgeom from bgs for COI 1700\n",
      "rebuilding COIgeom from bgs for COI 1800\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "COItractList = [muniTractList[c].copy() for c in range(nCOIs)]  #nomenclature\n",
    "print(\"compute and plot the COI pops by area\")\n",
    "COIpop =  [np.sum([tractPop[t] for t in COItractList[u] ]) for u in range(nCOIs)]\n",
    "COIarea = [np.sum([tractArea[t] for t in COItractList[u] ]) for u in range(nCOIs)]\n",
    "plt.scatter(COIarea,COIpop)\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.axhline(0.50*aDP,ls=\"dotted\")\n",
    "plt.show()\n",
    "isBigCOI, bigCOIlist = [0]*nCOIs,list()\n",
    "for u in range(nCOIs):\n",
    "    if COIpop[u] > 1.05 * aDP:\n",
    "        isBigCOI[u] = 1\n",
    "        bigCOIlist.append(u)\n",
    "nBigCOIs = len(bigCOIlist)\n",
    "print(\"there are\",nBigCOIs,\"COIs with pop > 1.05 *\",aDP)\n",
    "COIgeom = [tractGeom[COItractList[u][0]] for u in range(nCOIs)]\n",
    "for u in range(nCOIs):\n",
    "    if u%100 == 0:\n",
    "        print(\"rebuilding COIgeom from bgs for COI\",u)\n",
    "    for v in COItractList[u]:\n",
    "        COIgeom[u] = COIgeom[u].union(tractGeom[v])\n",
    "for c in bigCOIlist:\n",
    "    plotPoly(COIgeom[c])\n",
    "    plotCenter(r3(COIpop[c]/aDP), COIgeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "6e1c8383-3d2e-4f64-90ac-f7eecfe246cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now create units.  All 9 big COIs will be split into their component bgs\n",
      "splitting COI 0 with pop 216020\n",
      "splitting COI 27 with pop 146767\n",
      "splitting COI 40 with pop 152597\n",
      "splitting COI 691 with pop 256186\n",
      "splitting COI 958 with pop 130675\n",
      "splitting COI 981 with pop 144768\n",
      "splitting COI 1199 with pop 157730\n",
      "splitting COI 1499 with pop 161359\n",
      "splitting COI 1786 with pop 339264\n",
      "after splitting, the max unitPop/aDP = 1.038\n",
      "we now have 3099 units from the original 1850 COIs\n",
      "total number of bg assignments plus skips are now 9472\n"
     ]
    }
   ],
   "source": [
    "print(\"Now create units.  All\",nBigCOIs,\"big COIs will be split into their component bgs\")\n",
    "unitPop, unitGeom, unitTractList, unitCP, unitOrigCOIno = list(), list(), list(), list(), list()\n",
    "for u in range(nCOIs):\n",
    "    if isBigCOI[u] == 1:\n",
    "        print(\"splitting COI\",u,\"with pop\",COIpop[u])\n",
    "        for v in COItractList[u]:\n",
    "            unitPop.append(tractPop[v])\n",
    "            unitGeom.append(tractGeom[v])\n",
    "            unitTractList.append([v])\n",
    "            unitCP.append(tractCP[v])\n",
    "            unitOrigCOIno.append(u)\n",
    "    else:\n",
    "        unitPop.append(COIpop[u])\n",
    "        unitGeom.append(COIgeom[u])\n",
    "        unitTractList.append(COItractList[u].copy())\n",
    "        unitCP.append(COIgeom[u].centroid)\n",
    "        unitOrigCOIno.append(u)\n",
    "nUnits = len(unitPop)\n",
    "print(\"after splitting, the max unitPop/aDP =\",r3(np.max([unitPop[u]/aDP for u in range(nUnits)])) )\n",
    "print(\"we now have\",nUnits,\"units from the original\",nCOIs,\"COIs\")\n",
    "print(\"total number of bg assignments plus skips are now\",len(skipList) + np.sum([len(unitTractList[u]) for u in range(nUnits)]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7bdc0d4e-5f7a-43a7-a1cb-26d915a4bbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we found a total of 198 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "isContigUnit = [False]*nUnits\n",
    "nonContigUnits = list()\n",
    "for c in range(nUnits):\n",
    "    isContigUnit[c] = isContiguous(unitTractList[c],bgNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4f581295-9cc0-4f5d-83e0-6215de0202db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check - all bgs assigned?\n",
      "9472 9472\n"
     ]
    }
   ],
   "source": [
    "print(\"check - all bgs assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(unitTractList[uu]) for uu in range(nUnits) ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "dd8ffbfb-9340-4f8e-ace5-a58312522c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new for COI-Congrl - split out these noncontig units, like fragmented cities\n",
      "after splitting the 198 queen-adj units, we have 3334 units\n",
      "check - all bgs assigned?\n",
      "9472 9472\n"
     ]
    }
   ],
   "source": [
    "# SEE OH_615COIsnap99 for alt approach of maintaining queen adj except for crossovers\n",
    "print(\"new for COI-Congrl - split out these noncontig units, like fragmented cities\")\n",
    "for u in nonContigUnits:\n",
    "    nBGsAssigned, nBGsAvailable = 0, len(unitTractList[u])\n",
    "    contigList = getContigFromStarter(unitTractList[u][0],unitTractList[u],bgNbrs)\n",
    "    unitGeom[u] = tractGeom[contigList[0]]\n",
    "    for b in contigList:\n",
    "        unitGeom[u] = unitGeom[u].union(tractGeom[b])\n",
    "    unitPop[u] = np.sum([tractPop[b] for b in contigList])\n",
    "    unitCP[u] = unitGeom[u].centroid\n",
    "    remainList = list(set(unitTractList[u]).difference(set(contigList)) )\n",
    "    unitTractList[u] = contigList.copy()\n",
    "    nBGsAssigned += len(contigList)\n",
    "    while len(remainList) > 0:\n",
    "        contigList = getContigFromStarter(remainList[0],remainList,bgNbrs)\n",
    "        geo = tractGeom[contigList[0]]\n",
    "        for b in contigList:\n",
    "            geo = geo.union(tractGeom[b])\n",
    "        unitPop.append(np.sum([tractPop[b] for b in contigList]))\n",
    "        unitGeom.append(geo)\n",
    "        unitCP.append(geo.centroid)\n",
    "        unitTractList.append(contigList.copy())\n",
    "        nBGsAssigned += len(contigList)\n",
    "        remainList = list(set(remainList).difference(set(contigList)) )\n",
    "    if nBGsAssigned != nBGsAvailable:\n",
    "        print(\"oops, for unit\",u,\"only\",nBGsAssigned,\"were assigned out of\",nBGsAvailable)\n",
    "nUnits = len(unitPop)\n",
    "print(\"after splitting the\",len(nonContigUnits),\"queen-adj units, we have\",nUnits,\"units\")  \n",
    "print(\"check - all bgs assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(unitTractList[uu]) for uu in range(nUnits) ]) )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "399fd465-f53f-42b8-a103-e6b256b626f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have a total of 0 units with no bgs and 2087 units with 1 bg\n"
     ]
    }
   ],
   "source": [
    "nZero, nOne = 0,0\n",
    "for u in range(nUnits):\n",
    "    if len(unitTractList[u]) == 0:\n",
    "        nZero +=1\n",
    "    if len(unitTractList[u]) == 1:\n",
    "        nOne +=1\n",
    "print(\"we have a total of\",nZero,\"units with no bgs and\",nOne,\"units with 1 bg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "b9f7d2b5-5683-4ece-a4d7-4988aad8dbb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create the unit topology. First assign each bg's unit number (reverse of unitTractList)\n",
      "build unit nbr list based on adjacent bg's\n"
     ]
    }
   ],
   "source": [
    "print(\"Create the unit topology. First assign each bg's unit number (reverse of unitTractList)\")\n",
    "tractUnitNo = [-999 for bg in range(nTracts)]\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent bg's\")  #these are list-based, not geom-based\n",
    "for bg in range(nTracts):\n",
    "    u = tractUnitNo[bg]\n",
    "    for bb in bgNbrs[bg]:\n",
    "        uu = tractUnitNo[bb]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "05609139-6975-42a4-96b1-b72b9ed7d5bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's find singles and small clusters surrounded by big units\n",
      "This is a more generalized surround operation\n",
      "checking for big surround for unit 0\n",
      "checking for big surround for unit 500\n",
      "checking for big surround for unit 1000\n",
      "checking for big surround for unit 1500\n",
      "checking for big surround for unit 2000\n",
      "checking for big surround for unit 2500\n",
      "checking for big surround for unit 3000\n",
      "I found a total of 135 surrounded units. nSurrounders = 76\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's find singles and small clusters surrounded by big units\")\n",
    "print(\"This is a more generalized surround operation\")\n",
    "surrounded, surrounders = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"checking for big surround for unit\",u)\n",
    "    nbrNbrs = [len(unitNbrs[uu]) for uu in unitNbrs[u]]\n",
    "    bigNbrU = unitNbrs[u][nbrNbrs.index(np.max(nbrNbrs))]  #the surrounder should have more neighbors that surrounded\n",
    "    loopNo, foundSet = 0, set(unitNbrs[u]).difference({bigNbrU})\n",
    "    while len(foundSet) < 20 and loopNo < 7:\n",
    "        loopNo +=1\n",
    "        for uu in foundSet:\n",
    "            foundSet = foundSet.union(set(unitNbrs[uu]).difference({bigNbrU}) )\n",
    "    if len(foundSet) < 20:\n",
    "        surrounded.append(u)\n",
    "        surrounders.append(bigNbrU)\n",
    "print(\"I found a total of\",len(surrounded),\"surrounded units. nSurrounders =\",len(set(surrounders)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4db18589-f6d9-4b9b-8fe3-7321e1a79f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[406, 886, 1234] are both surrounders and surrounded\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zTTPuW74DFZeasfqx291KaK41Z0wMvnpyBuaMicHT/yzB99/cg/N1LTJES31BJXHyPX/CpIZIQaetp7GpYhNui70NYyLGKB2OXztzqQn3rdgBhxD450+mISU+TLayjcFa/PE74/Huj25F+cVG/Ofru1BV3ypb+eQ5ErhMgj9hUkOkgHZnO7ZUbEFLewsykzIRpAnq+kXktvN1LfjeG7sRotPg4yemIikyxCPnmTE8Ch8/MRVt7U78/OMDHjkHyUslcUFLf8I+NUR97JT1FMrryjF94HQEatix1NOsLXY8+OZuSBLwwaO3waT37D2PHxCMe28ZiE/3n/foeUg+bH7yH6ypIeojV/rO2Bw2zB40mwlNH3A4BX72j/241NiG9x+5DbHGvqkRC9Zp0GJ39Mm5qHdUEmcU9iesqSHqAxeaLmD/xf2YET+DTU196H+/KMO2E9V49+FbkeyhJqfOBGnVaG5r77PzkfskSFz7yY8wqSHysOKqYrQ727midh8z1zTjja0n8dSdwzF9WGSfnjtYp0ar3QmnU0Cl4vBub8aaGv/CpIbIQ9ocbcgz52Fc5DjEhsYqHU6/8+a2UzAEavBwalKfnztId3lx3xa7AyEBfJv1duwo7D/Yp4bIA8wNZhScLcDMxJlMaBRQ29SGDwvN+MHUJATr+j6puHLOv+86g5MXG/v8/NR9KknF5ic/wqSGSGa7K3ejrrUOswfNhlalVTqcfun9XWcgIPCDqYMUOf/w6FAMjgzBH74ow+8/O6pIDNQ9XKXbvzCpIZKJ3WHHhlMbMCRsCMZFjVM6nH6r1e7AOztO4/7JCYgIDVAkhkERIdj8i3QMM4XCGMTE1pupwJoaf8LGXiIZVLdUY++FvcgYlOF3tTPtDieOXWhEsbkO5+qa0WRzoNHWDq1awsCwIMR9vU1ICEOgVq10uPi46Czqmtvw4+mDFY1jz6kaHLU04BeZXP3Zq0nsU+NPmNQQ9VJZTRkutV7yq9FNTbZ2rC0+j/UHz2N/RR1a7A6oVRJiDIEIDdAgJECNNocTG0otqG22AwACtSpMHxqFu8fHYvaoaMU6yH5SdBazR0UjMULZNbQ+LDRjcGQIZo00KRoH3RxravwLkxoiN9kdduSdzUOyIRnT4qYpHY4sWtoceG/naawsOIm65jakDo3Ek3cOw4SEARg30Oga1XPta05fasLW4xfxeakFi1cXwxCowS+zRuJ7tyZC3YdDmqsbbTh4tg7/e9/4PjtnZ9randh42IKHpiVxSLeX45Bu/8KkhsgNVpsV289tx51Jd/pNc9PHe834fxvKUNfchvunJCB75lAMDOt6osAgnRqjYg0YFWvAY2lDYK5pxqubT+DZT0vxyV4zls0fh3Hxxj64AmDb8WoIcXkNJiXtOVWD+tZ2zBkbo2gc1DVJ4uR7/oQdhYl66FzjOeyv2o+5yXP9JqF5Le8EfvnJQUwfGoEtv0jH7xeM61ZC05mE8GD8v/tS8MkTU2Frd+Jbf92GX68thbXFLnPU18s/dhEjY/SI0ivTQfiKrScuIkofgNGxBkXjIOpvmNQQ9cDRmqOobKxEekI6JMk/mhX+suk4/rChDIszhuGl705AQrg8fVEmJ4Vj/aLp+P/uGoV/Fp1Fxov5+HT/OY9V9dsdTmw6cgGZY5SvHdlx4hKmD430m58RIl/BpIaom/Za9kKChMkxk5UORTYlZ63408ZjWJwxDE/eOVz2D2GNWoUf3zEYX/18Bm5LDkfOh8X43hu7cbq6SdbzAMCuk5dQ39qOzNHRspfdE0IInKhqxJg41tIQ9TUmNUTdsO3cNkSHRGNEuH8Nz/3DF0cxJCoEi2YN9eh5Yo1B+OsDk/Duj26FubYZWS8X4G9bT8LhlK/WZtXuCiSGByueTFxqakOL3YH4AcqOviLqj5jUEN2EEAJfnfkKIwaMQII+QelwZLX3dA22Hq/GzzNHQKPum7eCGcOj8EVOGv5zSiKe/+wIFry2HTtOVPe63O0nqvF5qQU5s4cp3uRT09QGAIjS6xSNg7qPo5/8B5MaohtwOB3YcHoDbou9DVHByo6m8YTX8soxzBSKrD7ugxISoMFvvzUGHz8+FRKA7/1tN/7z9Z0oPF3jVnnbT1Rj8er9mDo4AvMnDJQ32F5hfxpfUN1SjfCgcKXDIJlwSDfRDfzz+D9xz9B7EKBWdiSNJxw6b8Xmo1X40/3jFZtHZXJSOD7NTsWmI1V4ceMxfGfFTkxKDEPW2BjMGRODQREhN3ytEAJ7TtXg7e2nseGQBbcmhePV703knDDUY4cvHcb0gdOVDoNkwqSG6AYyEjOw9exWhAeGY6JpouLNGnJanleO+AFBuHt8nKJxSJKE2aOjMWukCV8csuCf+87hxS+P4fefHcXIGD1mj4rG4KgQxBgCoVJJKL/YiJKzVuwov4SKmmYkRQTjxe+Mx7cnDfSafx/V13HYHZx63xcIIaCS2GjhL5jUEN1ARFAEZg+ajbrWOmyu2IyYkBiMiRyjdFi9dqq6CZ+VVOK5e8ZC20d9abqiUkmYOy4Wc8fFormtHfllF/HFIQv+sacCl77uowIAKgkYZtJjxvAozB0bg6lDIrwmmbkiLiwQAHC+rkXhSKgr5xrPIS5U2cSe5MWkhqgLYYFhyBiUgcrGSmw6swnJYckYbFR2scTeWJlfjvCQAHznlnilQ+lUsE7jSnCAy8swXKhvhd3hRGJEMAI0yi+aeTPBOg0iQwNw5lKz0qFQF07UnsCMhBlKh0EyYlJD1E2xobGIDY1FeV05NlVswqjwUT73La/S2oJ/7juLX2SO8IoVtbsjSKdGUuSN+9f0mcqDQGPVN4+lr/9PrQWMCcCAJODrWqMRMaE4cLZOgSCpuzjiyT8xqSHqoSFhQzAkbAgOXzqMI5eOYIJpAiKCIpQOq1veKDiFIK0aD9w+SOlQfE9TFTBs9jePhbi8Oe1A7Rmg7HMgJBJIuBVZY2Lw238fxvm6FsS5udwEedbhS4cxOmK00mGQzLyjQZ3IB42OGI1ZibNQ0VCBgrMF2F+136u//VU32rBqzxk8lJqM0AB+n+k1SQJUKkATAEQNB0beBYREAacKsGBSPIK0aqzaXaF0lHQD1S3VfjlVQ3/HpIaoFyRJwkTTRKTFpyFBn4DN5s04WnNU6bA6tTK/HGpJwo9Sk5QOxX+FJwMAQkUTvj1pIFYXVsDW7lA4KLqWzWGDVu0fi9FSR/y6RiSTyKBIZCRmwNxgxqYzmzB0wFAMMnhHM0/pOSve2n4aORnDEBbcz2e6dTqBs3uA9tZOnvx6JJUkXf/3lrrulT8oFTjxFX4wdRre23kGn5dYMH+iN00KSPsu7MMt0bcoHQZ5AJMaIpkl6BOQoE/Asdpj2FSxCeMix8EUbFIsHrvDiV9+chDDo/V4In2IYnF4jYOrgRF3AbrQa574uulQiM7/rurm26VKDQiBoZHBmDE8Cs9/dgQzR5hgDGbNgLewO+1+OakmsfmJyGOGDxiOjMQMWJos2FSxCfVt9YrEsTyvHMcuNOB/70vxmnlpFBVqAoLCALXmmk17edPoLveT0QQA2kBAG3R560lzRfIdQPlm/OG+FDS02vHK5uMeuxzqmeqWaoQHclkEf9Wrd7gXXngBkiQhJyfHte/1119Heno6DAYDJElCXV1dl+Xk5uZiypQp0Ov1MJlMmD9/PsrKyq47bufOnZg1axZCQkJgMBiQlpaGlhZOcEXeLSUqBbMSZuHopaPIM+fB5rD12bnLLA14ZfNxPDFjMMYONPbZefs9XQgQHIFo2xk8OXs4/rbtFP7vYKXSUREuj3oaE+H7k2hS59xOagoLC7Fy5UqkpKR02N/c3IysrCwsXbq022Xl5+cjOzsbu3btwsaNG2G325GZmYmmpibXMTt37kRWVhYyMzOxZ88eFBYWYuHChVCp+M2TvJ8kSbg19lakDkzFnso92H5uOxxOz3YgbXc48d+fHMCgiBD8LGOYR89FnRg4CbCa8dikEHxrfBx+/nExSs9ZlY6q3xNCeN0s1CQfSbgxBrWxsRGTJk3Ca6+9hmXLlmHChAn485//3OGYvLw8zJw5E7W1tQgLC+tR+RcvXoTJZEJ+fj7S0tIAALfffjvuvPNO/O53v+tpuACA+vp6GI1GWK1WGAwGt8ogkktLewt2nd+FUF0oJkdP9sib7F82HcefvzqGf/5kGiYmDpC9fJ914itg6Oyuj5PLoTVoTZ6N+986gKp6Gz58/PabLtZJnnOs9hhCtCEYGMqO276kJ5/fblVzZGdnY968eZg92zNvDFbr5W8z4eGX2z2rqqqwe/dumEwmTJs2DdHR0ZgxYwa2bdt2wzJsNhvq6+s7bETeIkgThJmJMzEsbBg2mzfjUPUhWcvPP3YRf9p4DOMTwpjQKG3UPQg88Tn+9uBEBOnU+M/Xd+HMpaauX0eyq2ysZELj53qc1KxevRr79u1Dbm6uJ+KB0+lETk4OUlNTMXbsWADAyZMnAQC//e1v8eijj2LDhg2YNGkSMjIycPx45x3wcnNzYTQaXVtCQoJH4iXqjbDAMGQkZiAiKAKbzmzCybqTspT78V4zAODNH06RpTxPczgcePbZZ5GcnIygoCAMGTIEv/vd7zpMZtjY2IiFCxciPj4eQUFBGD16NFasWHFdWV7X906lAkbdDdPZDVj96G0I0qqx4LUd+MeeCjic3jtZo79pd7ZDrfKNpUGoF0QPVFRUCJPJJA4cOODaN2PGDLF48eLrjt2yZYsAIGpra3tyCvHEE0+IQYMGCbPZ7Nq3fft2AUAsWbKkw7Hjxo0TzzzzTKfltLa2CqvV6trMZrMAIKxWa4/iIepL5bXlYuPpjcLSaHG7DHNNkxiy5P/EyvwTMkbmWc8//7yIiIgQ69evF6dOnRIff/yxCA0NFS+//LLrmEcffVQMGTJEbNmyRZw6dUqsXLlSqNVqsXbtWtcxO3bsEAaDQeTm5orS0lJx9OhR8eGHH4rW1tZvTnZ8Y19e2jeaLglxeJ242NAqnly9Xwx6er246+UCsefUJWXi6Wf2VO4RjW2NSodBbrBard3+/O7RPDVFRUWoqqrCpEmTXPscDgcKCgrw6quvwmazQa12PxNeuHAh1q9fj4KCAsTHf7OCcGzs5dV6R4/uuE7HqFGjUFHR+TTkAQEBCAhQZh6CNkcbrDYrrDYrmtubERUUhajgKGi6O88F9VuDwwZjcNhgHLx4EKXVpbg19lbodfoelfH+zjMICdDgQR9a32nHjh245557MG/ePABAUlIS/vGPf2DPnj0djvnhD3+I9PR0AMBjjz2GlStXYs+ePfjWt74FAHjyySfxs5/9DM8884zrdSNGjOi7C7mZ4HAgcSoiT63Hn759Fx64fRD+59+H8J0VO/Gt8XF4Zu5IrhPlQc32ZoRo2ZfJ3/Wo+SkjIwMlJSUoLi52bZMnT8YDDzyA4uJitxMaIQQWLlyINWvWYPPmzUhOTu7wfFJSEuLi4q4b5n3s2DEMGuQdb9yfHPsE289tx/Zz23Hg4gHUtNYgVBeKQYZBaG5vxh7LHhScLUDB2QIcr+WcFXRzKVEpmJk4E9vO3bjfWGfa2p34pOgsvj1pIIJ1vpNET5s2DZs2bcKxY8cAAAcOHMC2bdswd+7cDsesW7cO586dgxACW7ZswbFjx5CZmQnAvb53fS4kEhg9Hyjfglt0FVjz01T8730p2FF+CRkv5uMvm46j1c5lFeRmtVl7/OWAfFRvq4WubX6qrKwU+/fvF2+88YYAIAoKCsT+/fvFpUvfVLHOmjVLvPLKK67HP/nJT4TRaBR5eXmisrLStTU3N7uOeemll4TBYBAff/yxOH78uPjVr34lAgMDxYkT3ati70n1lTu2nt3a7WPN9WaxpWKLKDAXiKa2Jo/EQ/6hJz9XQgix+egFMejp9eJIpW81szocDvH0008LSZKERqMRkiSJ3//+9x2OaW1tFT/4wQ8EAKHRaIROpxPvvvuu6/mdO3cKACI8PFy89dZbYt++fSInJ0fodDpx7NixbwpSqvnpWhePC3HoUyHaWkR9S5v4/WeHxdCl/yem5W4SeWVVSkfnV/LN+aLd0a50GOQmjzU/dceKFSvw3HPPuR5fGZL99ttv46GHHgIAlJeXo7q62nXM8uXLAcBVrXzF1a/JyclBa2srnnzySdTU1GD8+PHYuHEjhgzxvWnf4/XxiNfHw+60o+hCEVrbWzEwdCCGDeBcItQ7RadrERkagBHRvvWt9KOPPsIHH3yAVatWYcyYMSguLkZOTg7i4uLwwx/+EADwyiuvYNeuXVi3bh0GDRqEgoICZGdnIy4uDrNnz4bT6QQAPP7443j44YcBABMnTsSmTZvw1ltveWxwg9sihwIDkoDjXyLEMBCGwBAYg3Q4V9eC1XsqMGM4V5CWEzsJ9w9uzVPjizw9T822c9swfeB0t19vbjCjvK4cGpUGk0yTEKwNljE68lU9/bl68G+7EaRT440fTPZgVPJLSEjAM888g+zsbNe+ZcuW4e9//zuOHj2KlpYWGI1GrFmzxtXvBgB+/OMf4+zZs9iwYQNOnTqFwYMH4/3338eDDz7oOua73/0uNBoNPvjgg8s7zuwA7M2evygB15qYXamovIAZ/2eAgAofPT4VkwcNgErFCeLkcKb+DJzCiWRjctcHk1fqyee37zS6+7kriyCy9oauaGxrRKA6sEevabC1Y6APdjZtbm6+bnZwtVrtqn2x2+2w2+03PeZmfe+u7puDQdM8cAW9kzBEILv5GF7dcgIXG2xMaGR02noaMxJmKB0G9REmNTKRuvuVrAtalRa3x94OADjbcBZ55jyoJTVuib6FtTf9zL6qfUiNS+3Ra4K0KrS2+15H07vvvhvPP/88EhMTMWbMGOzfvx9/+tOf8KMf/QgAYDAYMGPGDPzyl79EUFAQBg0ahPz8fLz33nv405/+BODyUhS//OUv8Zvf/Abjx4/HhAkT8O677+Lo0aP45JNPlLy8LkmShJ9nDsfJ6kb89t+HcOfoaOg0XAKmt5zCySUR+hkmNV7s2r43LfYWJOgTMHTAUKVDIw9rtjdDJal63A8gUKv2ydEzr7zyCp599ln89Kc/RVVVFeLi4vD444/j17/+teuY1atXY8mSJXjggQdQU1ODQYMG4fnnn8cTTzzhOsaX+95JkoRFs4Zh7stbsfPkJfapkcHBiwcxPmq80mFQH2KfGplsP7cdqQN79q3aHeZ6M8qt5dCqtJgcMxkBamXm4iHP+uL0F5idOLvHSc0T7xehqqEVHz8xDWo2YfgcIQTG/fZLLJo1FI/P8P5EzNsdvnQYNa01N6xJFxDdqmUXEDAFmzB8wHC5Q6RuYJ8aP5ZgSECCIQFtjjbsvbAXbY42JBmSkGRMUjo0ksley16Mjxrv1miNWSNNePpfB3H/yp148TvjkRTJycZ8iSRJSIoMxqlqrg0lh9ERo7s+qJvyzHlManwAG21lIET3sn056dQ6TIubhvSEdDjhRJ45DzvO74DdYe/TOEheFfUV0Kl1iAmJcev1909JwEePT8XFBhvmvrwV7+08DSfXF/IpSREhTGq8UIg2BI1tjUqHAQA4WnMU7c52pcPwSkxqZKB0Z7TBxsFIT0jHJNMk7LHsQZ45D+YGs2LxkHua7c04XnccKVEpvSpnSlI4Pl98B+67JR6/XnsI339rN87VKbigI/XI4MgQnOYq3l5nkmkS9lXtUzoMHK05it2Vu9HmaFM6FK/EpEYGTjihkpS/lYGaQKQOTEV6Qjpa21uRZ87DrspdsDtZe+PthBDIP5uPmQkzZSkvJECD380fi/cfuRUnLzYh66UCfLTXjH7Shc6nJUWG4EK9DU02fhP3JmqVGk7hVDSGQ5cOweF09PqLjz9jnxoZCCG8Iqm52rABwzBswDA025ux8/xOOJwOjAwfidjQWKVDo05sP78d0+Kmyf5zdMewKGzIScP//Psw/vuTg/jykAW///Y4mPQ9m/+GALvDiX1natHmcMIpLv/eCwE4hXA9vvpPpxAQuPJYwOm8dt83r8VVj/dV1AEATl9qwpg4o4JXTNcaNmAYjtUeU6RvzaHqQ1BJKoyKHIX9Vfv7/Py+gkmNDJzC2ed9arorWBuMtPjLS1UcrTmKMnMZQrQhmGSaxGnDvURZTRlMwSYYAzzzAWYM0uLF+8djzphoLF1TgsyXCrBs/lj8R0qcR87nrz4vteBn/5D3w0QlXe4cfPWftnYnIkJ0CNTy99PbDAwdiHxzfp8nNZWNlWhztmGiaSIAsMb1JpjUyMApvKP5qSsjw0diZPhINLQ1YPv57XA4HRgbORZRwZwPQym1rbWobqnuk+kAMsfEYHJSOH71aQkWrtqPDaUW/O6esRgQovP4uf2Bvf1y08Pmn89AoFYNlSRBkgBJAlSS9PV2eSJOSYWOj6WrHl/1J/kenVqH1vZWBGr6prbT7rCjpLoEmUmZfXI+X8ekRgYC3tf8dDN6nR5p8WkQQuDQpUMorS6FMcCICaYJPnUdvs7hdGBX5S5kJWX12TnDQ3T46/cm4d8HK/Hsp6W486UCvPDtcZg9OrrPYvBVGvXlJCTaEIiQAL519leToydjj2VPn3wROWU9hZN1JzEzsWNfu8uNmNQZ/mbKQOnRT+6SJAljI8cCAKw2K7ae3QoBgZSoFIQHhiscnf/LO5uH9IT0Pv/ZkSQJ3xofh9uTw/HMv0rw4/f24ju3xOPZu0fDEKjt01h8iVZ9OeFv5xD5fk2r1sIhPD9r96FLh+B0OpExKKPT533xM6cv8Gu5DJzCCZWP30pjgBEzEmZgRvwMmBvMyDPnobiqmG23HlJcVYzhYcMRpFFu8UmTIRBv/nAy/nBvCj4vtSDrpQJsP1GtWDzeTvP1DM3tDmVHwJDyAtWBaGn33DQJhy4dAgCMixrX6fN8X74x3/4k9hLeOPrJXZIkYXzUeKQnpCPRkIiCswXIN+fDarMqHZrfqGyshEM4kGBIUDoUSJKE+6ckYEPOHRgUEYIH/rYbv15biuY2Die+Fmtq6IqUqBQcvHjQI2UfuXQEEMCYiDE3Pc5bB6cojc1PMnDCCX/8+QoPDMeMhBlwCieKq4pR31aPyKBIjIkYw6pPN7U52ryy01/8gGB88OPb8P6uM8j9/AgKjl3Ei/ePxy2D2Ax5xZW1tOysqen3AjWBsDlsspd75NIROITD1S2Aes4/qhcUppbUcDh9b2Xk7lJJKkyKnoT0hHREB0cj/2w+8s35aGhrUDo0n7O5YjNmJc5SOoxOqVQSfjgtCZ8vTkN4iA73rdiJ3M+P+OSq355wpaNwu4M1NXS5pkTOyfjKasrQ7mzvVkLDjsI3xpoaGRh0BtS31SsdRp+ICo5CenA6HE4H9lftR3N7MwAgOjgawwYM85tmOE/YVbkLk2MmQ6Py7l+75MgQfPzENLyx9ST+9OUxbDlahRe/MwHj4vv3RHDfND+xpoYuL5Z5qPrQDfu99ERZTRlsDluPZgpmbXnnvPvd1UdIktTv2jfVKjUmx0x2PbY0WbDj/A5XB7YBgQMwInwEtCqOpgGAk9aTMOgMiAyKVDqUblGrJDwxYwhmjjDhqY+KseC17Vg4ayiyZw51fbj3N66OwuxTQwAigiJcHXp7o6ymDK2OVoyPGi9DVMSkhmQRExLTYWXpmtYaFFoKXc1yobpQjI4YjQB1gFIhKqa+rR7mejNmJMxQOpQeGxGjx5qfpuLVLSfwyuYT2HSkCn+6fzyGReuVDq3PuWpq2PxEX9OqtL2aiO/wpcNwCqdbCU1/+yLdXUxqyCPCA8MxLW6a63FDWwOKq4pdK8sGagIxOmI0QrQhSoXYJ5zCia1nt+Ku5LuUDsVtOo0KT905HLNHmfDURwcw75Vt+EXmcDwyfbCr82x/cKVPDTsK0xWToieh6EJRh/e67jpUfajDXGE9wSHdN8akhvqEXqfHbbG3uR63tLfg8KXDaLZf7pOjUWkwOmK0x9Y/UsoW8xbMTJjpF+3fKfFhWL9oOv74RRlyPz+KjYcv4I/fGY9BEf6dmF7B5ie6VoA6AHaHvcevO3jxIHRqHUaGj3T73Kyp6RyTGlJEkCYIt0Tf4npsd9hxuOYw6m2XO1yrJBWGDxju0+tSFVcVY/iA4QjWBisdimwCtWr86j9G487R0fjFJweQ9eetWDpvFB68LdEvEreb0aguNz+xpoauFhkUiYvNF7v9XlVcVYxgbbAiK317ypFLR3Cs9hjuGXqP0qEwqSHvoFVrO7QrO5wOHKs9hqM1R13DF0dHjPaZjrZnG85CQCBBr/wEe55w2+AIbFicht9/dgTPflqKLw9Z8If7UhBrVG6GZE/jkG7qzOiI0Sg4W4AZwV33mdt3YR8MOgOGDhjaq3N6y5DuC00XcLD6IEaGj8S4qHH45NgnuG/4fYrG1D+HMZDXU6vUGBUxCnfE34G0+DTcMfAOVNRXYPu57V7fntxsb8bRmqOYaJqodCgeFRKgwfMLxuHdH92KYxcakPlSAf6176zX//u4i0O6qTOSJHWrlrLQUoiwwLBeJzTfnFieYtxhc9iwuWIzKpsqceegO5GgT0Cbo82tvkVyY1JDPkGSJEyKnoTREaPx5ZkvvfaDUwiBPHOe106w5wkzhkfhy5wZmD0qGk99dACPv1+E6kb5Z1tV2jdrP3nnzx4pp6v5ubae3YqY4BgMNg6W5XxK1dQIIbC7cjd2V+5GWnwaJpgmuJ5rbW+FXqf8qEgmNeRTBgQOwPSB0/H5qc+9chbnree2InVgar+bhNAYrMVL352AFQ9OQtGZWmS+VIANpZVKhyUrDdd+ohvQqDRod16/XprD6cCG0xswOmK07Gu99XVH4bKaMmyu2IyR4SORFp923SSiAsIrOi+zTw35nBBtCGYlzsLnpz/HnKQ5XjPB36FLhxAfGu93I7h6ImtsLCYnhWPpv0rwxN/3YcHEgfjt3WNgDPaOf6Pe0HDtJ7qBYE0wmtubYdAZXPua7c3YYt6CjMQMt+exuREJEvZU7oFK1fmXp86SCwkSJsdM7vEXLkuTBaXVpRgxYAQyBmXc8DhvWdiZSQ35pEBNIOYkzUHB2QLEhMR0uaKtp1U1V6GprQljYpWNwxtEhgZg5fdvwZr95/CbdYewo7wa/+/eFKSPMCkdWq+wozDdSLAmGM32b5Kak3Uncbr+NOYmz/XIB/2tMbfCITqvqb5R01TRhSLYHDYEabrXmb/Z3oyd53fCFGzC7EGzuzxeznWweoNJDfksrUqLjMQMnGs8hy0VWxCqC8Ut0bf0+beFNkcb9lXtQ1ZSVp+e15tJkoRvT4rH1CER+O9PDuKhtwvxvdsSsfSuUQgN8M23Ha2KHYWpc8HayzU1QghsPbcVpmCTR/vVSZIEjdSz3yO1pO5W85DD6cCuyl2QIGFm4sxuv58KsKbGr3jLELv+aGDoQAwMHYj6tnpsPbsVADA5ZnKfzVa8uWLzTatl+7NYYxDe+9Gt+GB3BX7/2RFsPX4Rf7xvPG4bHKF0aD2mUklQSYCdNTV0jWBNME5ZT6GspgypA1M7NEN5CyFEl6O0Dl48iIstFzE1dmqP59di85Of8YYOUv2dQWfAjIQZcDgd2GPZg5b2FkyOmezRN5gd53dgcsxkr+nX440kScKDtw/CHcMi8YuPD+A/39iFR1KT8Ys5IxCoVSsdXo9o1Co42FGYrhGiC0GDvQFZSVleOwnlzTryVtRX4FjtMYyNHNujlcKv5oTTK66dSQ35HbVKjalxU+EUThRdKEJ9Wz0mmSZhQOAAWc9zvPY4wgPDfWZCQKUNigjB6sem4q1tp/C/X5ZhS1kVXrx/AiYkhCkdWrdpVRI7CtN1tCqt1zc/d5bUWG1WFFoKkaBP6Fa/mZtxCidUXjCgWvkIiDxEJakwJWYKZiXMwinrKWyq2ISLzRdlKbu2tRYXmi/0au2W/kitkvBo2mD836LpCAnQ4N7lO/DHL8rQ1u4biYJGreKQbvJJVzc/2R125Jvzcaz2GDISMzAifIQMJwBraoj6wpWJ+4QQKKkuQUl1CUZHjEZMSIxb5TmcDuw8vxNzk+fKHGn/MSxaj3/+ZBqW55XjL5uOY9PRKrz4nfEYHed9fRGuplFJaGdNDfmgKzU1hZZCNNmbMC1uGnRqnWzlO+H0im4YvaqpeeGFFyBJEnJyclz7Xn/9daSnp8NgMECSJNTV1XVZTm5uLqZMmQK9Xg+TyYT58+ejrKyswzHp6emu6aivbE888URvwqd+RpIkpESlYFbiLNS01mBzxWaYG8w9LmezeTNmJc7yim8lvkyrVuFnGcPwaXYqhBC456/b8NctJ7w6adCoJXYUJt8kgA2nN2BI2BCkJ6TLmtAAl/uVesN7ottJTWFhIVauXImUlI6dipqbm5GVlYWlS5d2u6z8/HxkZ2dj165d2LhxI+x2OzIzM9HU1NThuEcffRSVlZWu7Q9/+IO74VM/NzpiNGYlzkKzvRmbKzbjlPVUt16317IXYyPGyj6ZVn82dqARaxem4tE7BuPFL8tw74qdOFHVqHRYndKoVBzSTT5pSuwU3JV8F8IDwz1SvreMAHar+amxsREPPPAA3njjDSxbtqzDc1dqbfLy8rpd3oYNGzo8fuedd2AymVBUVIS0tDTX/uDgYMTEuNdkQNSZEeEjMCJ8BE7WncSWii1I0CfccMG509bT0Kl1iA2N7eMo/V+ARo3/zhqJ2aOj8YuPDmDeX7bil3NG4EepyVCplP/2d4VWLbFPDfmk/jJC062amuzsbMybNw+zZ/eut/SNWK1WAEB4eMeM8oMPPkBkZCTGjh2LJUuWoLm5+YZl2Gw21NfXd9iIbmRw2GDMTJwJnVqHzRWbcbTmaIfnG9sacdJ60u3hjtQ9kxIH4P9+dgceuG0Qlv3fEfznG7tQcenGv+d9TaNWcUZhok54Q38awI2amtWrV2Pfvn0oLCz0RDxwOp3IyclBamoqxo4d69r/ve99D4MGDUJcXBwOHjyIp59+GmVlZfjXv/7VaTm5ubl47rnnPBIj+a9EQyISDYk433gemys2IzYkFiPCRyD/bD7uSr5L6fD6hSCdGr++ezTuHB2NX35yAFkvF2DpXaPwwG2JirfZs6MwkXfrUVJjNpuxePFibNy4EYGBnulTkJ2djdLSUmzbtq3D/scee8z193HjxiE2NhYZGRkoLy/HkCFDritnyZIleOqpp1yP6+vrkZAg7yqpV/OW9kSSR1xoHOJC43C+8Ty+OP0FZibMVPwDtb+ZOiQCG3LS8PvPjuBXn5bii0MW/L97UxAX1r21azxBo5ZgZ/MTkdfqUfNTUVERqqqqMGnSJGg0Gmg0GuTn5+Mvf/kLNBoNHI7OF9jqroULF2L9+vXYsmUL4uPjb3rsbbfdBgA4ceJEp88HBATAYDB02Ih6Ki40DnOT5/Z4ynCSR2iABr9fMA7vPDwFxy80Ys6fC/DPorMQQpnEQqNSsaaGyIv1KKnJyMhASUkJiouLXdvkyZPxwAMPoLi4GGq1e1OeCyGwcOFCrFmzBps3b0ZycnKXrykuLgYAxMay0yaRv0sfYcIXOWm4c1Q0fv7xATz2fhEuNtj6PA6tWmKfGiIv1qPmJ71e36GfCwCEhIQgIiLCtd9iscBisbhqUEpKSqDX65GYmOjq+JuRkYEFCxZg4cKFAC43Oa1atQpr166FXq+HxWIBABiNRgQFBaG8vByrVq3CXXfdhYiICBw8eBBPPvkk0tLSrhtSrhRv6SRF5K+MwVr86bsTkDkmBv/fmhJkvpSPZfPHYV5K332x0ahUbH4i8mKyL5OwYsUKTJw4EY8++igAIC0tDRMnTsS6detcx5SXl6O6utr1ePny5bBarUhPT0dsbKxr+/DDDwEAOp0OX331FTIzMzFy5Ej8/Oc/x7333ot///vfcodPRF4ua2wMvnwyDbcPjkD2qn1Y9I/9qG1q65Nza9QSHJynhshrSUKpxuk+Vl9fD6PRCKvV6pH+NdvPbUfqwFTZyyWizgkhsO7Aefx67SHoNCq88O1xyBgV7dFz/uidQqhVEt74wWSPnofI13jyM7Ann99c0FImEiQ4Bb/BEfUVSZJwz4SB+PLJNIyNM+CRd/filx8fQH2r3WPnVHNIN5FX44KWMtGqtWhztHH6fKI+Fm0IxFsPTcFHe8343foj2H6iGn+4bzymD4uU/VxatYSRe7egceiVTspX+tKJq/4OtF+qhvGeezgNAFEfY1IjE51ahzZnGwLBpIaor0mShO9OSUTq0Ej89ycH8eCbu/H92wdhyV0jEayT721Oo1KhJmQAQu+446bHNe/bB2dTM9ShIbKdm4i6xuYnmehUOrQ5+qazIhF1Ln5AMP7+yG147ltj8HGRGXNf3orC0zWylX+5o3DX3RDVBgOc9VbZzkvk7SRIis0fdTUmNTLRqXWwOzzXlk9E3aNSSfjhtCR8vjgNkaEBuH/lTvz+syNotfduclAA0KpU6M6IbrXRCAfXm6N+orW9FZdaL3nFzPpsfpKJTnW5+YmIvENyZAg+enwq/rb1JF788hg2H63Cn+4fj5T4MLfL1KgltHZjSLdKr4fj1Cm3z0PkLewOOy40X4ClyQKb4/oJLwUEAtQBGB81HipJ+XoSJjUy0aq1aGhrUDoMIrqKWiXh8RlDMHOkCU99VIwFr+1AdvoQLJw1DDpNz9+AtWoVmrpRVSMFBKD9wgU0bt3WxZFXyrpRh2IBCAHR1gb97Nk9CZWoS+3OdlS3VMPSZEFDW0OnHdu1Ki1MwSaMjhjtE8vFMKmRyZWOwkTkfYZH67Hmp6n465YTeHXzCXx1pAp/+u54jIzp2ZxVapWE9u4kNZIE4913uxvuddovXkTj1q1ddlAmusIpnKhprYGlyYI6W51r1vurm4hUkgpRQVEYEjYEep1eqVBlxaRGJjoV+9QQeTOtWoWc2cMxe1Q0nvqoGHe/sg1P3jkcj90xGBp192ptNGoJDgU6Q2qioqC+UAV7ZSW0XO+OAFhtVlQ2VaKmpabTviwSJIQHhSM+NB5jIsb0m+kFmNTIRKfm6CciXzB2oBH/XjQdL208jj9+UYaNhy/gxe+Mx+Co0C5fq1Wp4FBoQcvAMaPRtG07kxoCAGyu2Iw74u/A8AHDvaIvi7fgnZCJVqWF3cmaGiJfEKBR45m5I/HxE1NR29SGuS9vxVvbTsHZRdOSRi3BqdCw1cvftJUfXULeISo4CpFBkUxorsG7IZP+UrVH5E9uGRSOzxen4b9uTcT/rD+M/3pjF8w1zTc8XqtWoV3RVRL4PkN0M0xqZOQNY/SJqGeCdGr89ltjsOrR23C2tgVZfy7AP/ZUdDqRmEbFVbrJO3jLZHfehkkNERGAaUMisSHnDtw9Pg5L/lWCh94uhMXa2uGYJls7NGrWlpDyooKjcKH5gtJheB0mNUREX9MHavHCvSl4+6EpOFJZj8yX8vHp/nMQQqDV7kDxVzsQNW6M0mESYWjYUBRXFaO2tVbpULwKRz8REV1j5kgTvnwyDb9Zdwg5HxZjQ6kFusY6BFaexY9/fp+CkbG5gS5TSSrMSZqDHed3IC40DsnGZKVD8gpMaoiIOhEWrMPL/zkRc8bE4FeflmLaoXzc/+tFGB6tzCRlQgjYysvRZWdhSQK+7msResd0zwdGipEkCakDU5Fvzkd0cLRPzPjraUxqZDQgYABqWmsQHhiudChEJJO7xsXituRwVJ4eisQz+9Aeezs0Awb0eRySJCHioYe6fXzj1m0QDgcktdpzQZFXmBIzBYcvHcbkmMlKh6I49qmR0ZCwISivK1c6DCKSWURoAMaOTYY+Kwu2o0fRXFSkdEhdChwzGq2HDikdBvUBtUoNp+CoPIBJjawCNYGdrmJKRP5BkiSETJ0KbXw86j//HI4G713EVhMeDkctO5H2BxIkTinyNSY1REQ9pI2Ohj4rCy3FB9BSXKx0ONTPMan5BpMamUmc8ZOoX5AkCaF3TIcmKgr1GzbA2dSkdEjXUYWEwNHYqHQY5GkSOBHf15jUEBH1gnbgQOgzM9FUWIiWUu/qwxI4bhxaS0uVDoM8jDU132BSIzO1Sg27gwtbEvUnkkoFfXo61PpQ1G/4As7W1q5f5GFCCLSWlABc8NDvSZA4hdHXOKRbZtHB0bA0W5CgT1A6FCLqY7pBg6CNj0djfgG0cbEIHDmyz2NwWK1o3r8fcAoETZygyPBz6luSxJqaK5jUyCw2JBYl1SVMaoj6KUmthn7WTNhOnkT9l19Cn54OSafz6DmFEGgpLoajrg5qoxGhaWmQVKyh6S/Y/PQNJjUy47BuIgKAgMGDoYuPR0N+PgKSkxEwdKjs52i/eBEtJaWABARNYK1MfyVJHKByBZMaD+AIKCICAEmng+HOO9FaVoaGTZsQOmMGJE3v3naFw4HmvUVwNjVBExWJ0Jnp/FAj+hqTGiIiDwscMQK6pCQ0bN6MgKHDEDC454sPtp09B9uxY5DUKgTdcgvUoaEeiJTItzGpISLqA6qAABgyM9F6+DAaNm9B6Iy0Ltdlcra1obmwEMLWBu3AOOhnzeyjaIl8E5MaIqI+FDh6NHSDB6Phq00IHDUSusTE646xlZej7UwFJK0WwVMmQxUYqECk5EvY7eEyJjVERH1MFRgIw5xMtJSUoHHrVoRMnw7R0oLmvXsh2tsRMHgwa2WI3MCkhohIIUHjxsHR0ICGjRuh1usRMnUqJK1W6bCIfBaTGiIiBan1ehgyM5UOg8gv9Gp2phdeeAGSJCEnJ8e17/XXX0d6ejoMBgMkSUJdXV2X5eTm5mLKlCnQ6/UwmUyYP38+ysrKOj1WCIG5c+dCkiR8+umnvQmfiIiI/IjbSU1hYSFWrlyJlJSUDvubm5uRlZWFpUuXdrus/Px8ZGdnY9euXdi4cSPsdjsyMzPR1Mmqt3/+8585JwMRERFdx63mp8bGRjzwwAN44403sGzZsg7PXam1ycvL63Z5GzZs6PD4nXfegclkQlFREdLS0lz7i4uL8eKLL2Lv3r2IjY11J3QiIiLyU27V1GRnZ2PevHmYPXu23PEAAKxWKwAgPDzcta+5uRnf+9738Ne//hUxMTFdlmGz2VBfX99hIyIiIv/V45qa1atXY9++fSgsLPREPHA6ncjJyUFqairGjh3r2v/kk09i2rRpuOeee7pVTm5uLp577jmPxEhERETep0dJjdlsxuLFi7Fx40YEemgyqOzsbJSWlmLbtm2ufevWrcPmzZuxf//+bpezZMkSPPXUU67H9fX1SEjgytlERET+qkdJTVFREaqqqjBp0iTXPofDgYKCArz66quw2WxQdzHt980sXLgQ69evR0FBAeLj4137N2/ejPLycoSFhXU4/t5778Udd9zRaf+dgIAABAQEuB0LERER+ZYeJTUZGRkoKSnpsO/hhx/GyJEj8fTTT7ud0AghsGjRIqxZswZ5eXlITu642NszzzyDH//4xx32jRs3Di+99BLuvvtut87pKU7h5HTVRERECuhRUqPX6zv0cwGAkJAQREREuPZbLBZYLBacOHECAFBSUgK9Xo/ExERXx9+MjAwsWLAACxcuBHC5yWnVqlVYu3Yt9Ho9LBYLAMBoNCIoKAgxMTGddg5OTEy8LgFSmsPpgEbFOQ2JiIj6Wq8m3+vMihUrMHHiRDz66KMAgLS0NEycOBHr1q1zHVNeXo7q6mrX4+XLl8NqtSI9PR2xsbGu7cMPP5Q7PI9rF+1Qq9xvgiMiIuopAaF0CF5BEkL0iztRX18Po9EIq9UKg8HgsfM0tjXiRN0JTDBN8Ng5iIiIrrbt3DZMHzhd6TA8oief37LX1PR3zJaJiIiUwaRGZg1tDdCpdUqHQURE1O8wqZFRm6MNpdWlGB0xWulQiIiI+h0mNTLaXLEZsxJnKR0GERFRv8SkRiZfnv4Sk2Mmczg3ERH1Oc6PdhmTGhkIIaBT6xAZFKl0KERERP0WkxoZVLdUIyak65XDiYiIyHOY1Migwd4Ag85zc98QERFR15jUEBER+TjOkXYZe7USERG5qcnehEstl2B32mF32tHubP/mT4cdTjj7JI4kQ1KfnMfbMakhIiJy0+7K3RgaNhTBmmBoVBpoVVpo1VpoVBpoJA3XAuxjTGqIiIjcpFPrkGhIVDoM+hr71MiBTZlERP0S54fxLqypkQl/sIn8y1dnvkKgJtD1WAgBU7AJScYkBKgDFIyMiG6ESQ0RUScC1AGYPnC667EQAtUt1Th48SBsDhuEEJCky19mooKikGxM5mK2RApjUkNEdI2rE5YrJElCVHAUooKjrju2qrkK+6v2w+60d3guLiQOCYYEaFVaj8dMRExqiIiuU9FQgUR99zp/SpKE6JBoRIdEd9jvFE5YmizYa9kLh3C49qugwkD9QMSHxnNkjB/g/DDehUmNDPhDTeRfKuorOjQ9uUMlqRAXGoe40LgO+x1OB841nsPuyt0d5jCRIGFg6EDE6+O5MC6Rm/ibI5Nrq6qJyLd56ndarVIj0ZB43TBgh9OB803nsceyB07RMdmJDY1Foj6RyY4X4iAR78LfECIiL6BWqZGgT0CCPqHDfqdw4nxjx2RHiMu1w1cSrysTvWnVWmhV2m8mgVNd9fiq5zSSxie/iDmF8/LMvY7Ls/e2Odou/+lsc+27MpuvEAKu/77+++X/Xf7vRsnIzZ679jgAsLXbZL1G6h0mNURE11BJ3jOFl0pSIV4fj3h9fKfPCyHgEI4OU/Rf+YBvd7ajpb2l43NOOxxOR6dldSj36w9tb6qJkCC5krMrf+pUOgSoA6DX6i8/VuugklRQSarLsUuXXydBgiR98yf5JyY1RETX8KV+cpIkQSNp2DRFBM4oLIsrVcFE5B+8qXaCiLqPSQ0RERH5BSY1RERE5BeY1BAREZFfYFJDREREfoFJjQx8aaQEERGRv2JSIxOOliAiIlIWkxoiIiLyC0xqiIiIyC8wqSEiIiK/wKSGiIiI/AKTGhmoJBVHQBERESmMSY0M1JIadqdd6TCIiIj6tV4lNS+88AIkSUJOTo5r3+uvv4709HQYDAZIkoS6urouy8nNzcWUKVOg1+thMpkwf/58lJWVdTjm8ccfx5AhQxAUFISoqCjcc889OHr0aG/Cl41GpYHD6VA6DCIion7N7aSmsLAQK1euREpKSof9zc3NyMrKwtKlS7tdVn5+PrKzs7Fr1y5s3LgRdrsdmZmZaGpqch1zyy234O2338aRI0fwxRdfQAiBzMxMOBzKJxMalQbtznalwyAimXDeKSLfpHHnRY2NjXjggQfwxhtvYNmyZR2eu1Jrk5eX1+3yNmzY0OHxO++8A5PJhKKiIqSlpQEAHnvsMdfzSUlJWLZsGcaPH4/Tp09jyJAh7lyGbDQqDZufiPwI+8gR+Sa3amqys7Mxb948zJ49W+54AABWqxUAEB4e3unzTU1NePvtt5GcnIyEhIROj7HZbKivr++weYpep0d9m+fKJyIioq71OKlZvXo19u3bh9zcXE/EA6fTiZycHKSmpmLs2LEdnnvttdcQGhqK0NBQfP7559i4cSN0Ol2n5eTm5sJoNLq2GyU/cghQB6DN0eax8omIiKhrPUpqzGYzFi9ejA8++ACBgYEeCSg7OxulpaVYvXr1dc898MAD2L9/P/Lz8zF8+HDcf//9aG1t7bScJUuWwGq1ujaz2eyReK+QJLbBExERKalHfWqKiopQVVWFSZMmufY5HA4UFBTg1Vdfhc1mg1qtdjuYhQsXYv369SgoKEB8fPx1z1+pdRk2bBhuv/12DBgwAGvWrMF//dd/XXdsQEAAAgIC3I6FiPovdhQm8k09SmoyMjJQUlLSYd/DDz+MkSNH4umnn3Y7oRFCYNGiRVizZg3y8vKQnJzcrdcIIWCz2dw6p9z4JkjkP9hRmMg39Sip0ev11/VzCQkJQUREhGu/xWKBxWLBiRMnAAAlJSXQ6/VITEx0dfzNyMjAggULsHDhQgCXm5xWrVqFtWvXQq/Xw2KxALhcMxMUFISTJ0/iww8/RGZmJqKionD27Fm88MILCAoKwl133dW7OyAjIQSboYiIiBQi+4zCK1aswMSJE/Hoo48CANLS0jBx4kSsW7fOdUx5eTmqq6tdj5cvXw6r1Yr09HTExsa6tg8//BAAEBgYiK1bt+Kuu+7C0KFD8d3vfhd6vR47duyAyWSS+xLcEqAOgM3hHbVGRERE/ZEkhOgX9az19fUwGo2wWq0wGAyyl19WU4bwwHBEBUfJXjYR9a0d53Zg2sBpSodBROjZ5zfXfpKJQWdAQ1uD0mEQERH1W0xqZBKqC0WDnUkNkT9gR2Ei3+TWMgl0vRBtCJramro+kIi8Xk1rDcpqvllUt7Mk59qW+86OiQmJQXhg5zOjE5H8mNTIRCWp+O2OyE+kxad1az23a6dyuHb0Y2l1KdLi02SNjYhujEkNEdE1jAFGWcpRS+5PRkpEPcc+NTJiTQ0REZFymNTIiLMKExERKYdJDREREfkFJjVERETkF5jUEBERkV9gUkNERER+gUmNTPrJElpERERei/PUyMTmsEGn1ikdBhF5gXZnO/ZY9kCv0ysdClG/wqRGJjaHDUGaIKXDICIF2Z127Kncg3ZnO6bETEGwNljpkIj6FSY1Mmlpb0GAOkDpMIhIAXaHHbsqd0FA4NaYWxGoCVQ6JKJ+iUmNTGwOG9/IiPoZm8OG3ZW7AQC3x97OJmgihTGpkUlreytX4yXqJ1raW7Cncg/UKjWmxk2FVqVVOiQiApMa2bQ6WllTQ+Tnmu3N2GPZA51Kh9SBqdCo+BZK5E34GymT1vZWBKqZ1BD5o8a2RhRaChGoCcQdA++AWsXVt4m8EZMambQ72/mtjcjPWG1WFF0oQog2BDMSZkAlcWovIm/GT2EZSRJX6SbyB3WtddhXtQ96nR4zE2byd5vIRzCpISL62qWWSyi+WIwBAQOYzBD5ICY1RNTvCSGQfzYfYQFhmJUwi8kMkY9iUkNE/VpLews2VWzCzISZCNGGKB0OEfUCkxoi6reqmqtw4OIBzE2ayxFNRH6ASQ0R9UvHao+hprUGdw66U+lQiEgmHJ9IRP1OoaUQDqcDt8fernQoRCQj1tQQUb/hFE5srtiMMRFjEBsaq3Q4RCQzJjVE1C+0tLdgc8VmzEyYiWBtsNLhEJEHMKkhIr93oekCDlYfxNzkuZwVmMiPMakhIr9WVlOGOlsdOwQT9QP8ykJEfmtP5R4ICNwWe5vSoRBRH2BNDRH5nSsdgsdGjkVMSIzS4RBRH2FSQ0R+pdnejC3mLewQTNQP9ar56YUXXoAkScjJyXHte/3115Geng6DwQBJklBXV9dlObm5uZgyZQr0ej1MJhPmz5+PsrIy1/M1NTVYtGgRRowYgaCgICQmJuJnP/sZrFZrb8InIj9jabJgx/kdmJs8lwkNUT/kdlJTWFiIlStXIiUlpcP+5uZmZGVlYenSpd0uKz8/H9nZ2di1axc2btwIu92OzMxMNDU1AQDOnz+P8+fP449//CNKS0vxzjvvYMOGDXjkkUfcDZ+I/ExZTRkq6iswe9BsjnAi6qckIYTo6YsaGxsxadIkvPbaa1i2bBkmTJiAP//5zx2OycvLw8yZM1FbW4uwsLAelX/x4kWYTCbk5+cjLS2t02M+/vhjPPjgg2hqaoJG03UrWn19PYxGI6xWKwwGQ4/i6Y7t57YjdWCq7OUSUdf2VO6BMcCIEeEjlA6FiGTWk89vt77OZGdnY968eZg9e7ZbAXblSrNSeHj4TY8xGAw3TGhsNhvq6+s7bETkX5zCia/OfIVEQyITGiLqeUfh1atXY9++fSgsLPREPHA6ncjJyUFqairGjh3b6THV1dX43e9+h8cee+yG5eTm5uK5557zSIzXcqOyi4h6iR2CiehaPaqpMZvNWLx4MT744AMEBgZ6JKDs7GyUlpZi9erVnT5fX1+PefPmYfTo0fjtb397w3KWLFkCq9Xq2sxms0fiBQCbwwadWuex8omoI0uTBTvP72SHYCLqoEc1NUVFRaiqqsKkSZNc+xwOBwoKCvDqq6/CZrNBrVa7HczChQuxfv16FBQUID4+/rrnGxoakJWVBb1ejzVr1kCr1d6wrICAAAQEBLgdS0/YHDYEaYL65FxE/V15XTkutVxCxqAMpUMhIi/To6QmIyMDJSUlHfY9/PDDGDlyJJ5++mm3ExohBBYtWoQ1a9YgLy8PycnJ1x1TX1+POXPmICAgAOvWrfNYTZE7WtpbEKDumwSKqD87bT2NOlsdbo29VelQiMgL9Sip0ev11/VzCQkJQUREhGu/xWKBxWLBiRMnAAAlJSXQ6/VITEx0dfzNyMjAggULsHDhQgCXm5xWrVqFtWvXQq/Xw2KxAACMRiOCgoJQX1+PzMxMNDc34+9//3uHjr9RUVG9qh2Sg81hQ6DGe5IsIn90tuEsLjRf4JIHRHRDss8ovGLFig4ddK8MyX777bfx0EMPAQDKy8tRXV3tOmb58uUAgPT09A5lXXnNvn37sHv3bgDA0KFDOxxz6tQpJCUlyXwVPdPa3orwwBuP1CKi3qluqcbp+tOYPnC60qEQkRdza54aX+TJeWoOXDyAwcbB0Ov0spZLRECbow1bzFswJ2mO0qEQkQI8Pk8NXU+gX+SGRH1KCIFNFZuQkchOwUTUNSY1MgjVhqLZ3qx0GER+p+BsAabFTYNGxbV3iahrTGpkoFVpYXfYlQ6DyK8cuHgAScYkGAOMSodCRD6CSY0MtCot7E4mNURyMdebIYTAIMMgpUMhIh/CpEYGWrUWbc42pcMg8gsNbQ04UXcCE0wTlA6FiHwMkxoZsPmJSB4OpwMFZwuQnpCudChE5IOY1MiAzU9E8ths3oyMxAxIkqR0KETkg5jUyECrZlJD1Ft7KvdgXOQ4zs5NRG5jUiMDjaRBu7Nd6TCIfNbx2uPQ6/SICYlROhQi8mFMamTAqnIi951rPIea1hqMihildChE5OOY1BCRYi61XEJ5XTkXqSQiWTCpISJFNLY1Yu+FvUiLT1M6FCLyE0xqiKjPtTnakH82H5mDMpUOhYj8CJMaIupTDqcDX575EnOS5rA/GhHJikkNEfUZIQQ2ntmI2YmzuUglEcmOSQ0R9ZnN5s2YGjeVc9EQkUcwqZGBEELpEIi83vZz2zEuchxX3SYij2FSIwOHcECtUisdBpHX2mvZiwR9AkzBJqVDISI/xqRGBu3OdqglJjVEnTlUfQiGAAMSDYlKh0JEfo5JjQwcwgGtSqt0GERe50TtCTiFE8MHDFc6FCLqB5jUyIA1NUTXO9twFrW2WoyLGqd0KETUTzCpkUG7s53DU4muUt1SjZPWk5gSM0XpUIioH2FSI4N2Zzs7ChN9rba1FgcuHuDyB0TU51i9IAOHcLCmhgjAaetpnKk/g4zEDKVDIaJ+iJ/EMmh3tkMj8VZS/yWEwG7LboRqQzEjYYbS4RBRP8VPYhk4hZM1NdRvWW1W7Dy/E7fG3orwwHClwyGifoyfxDIQ4IzC1D8dqj4Eq83KxSmJyCuwo7BM+IZO/YndacfGMxuh1+kxbeA0/vwTkVdgTY0MWFND/Umbow1fnvkScwbNgVbNSSeJyHswqZGJBH5Tpf5h69mtyErKYj8yIvI6bH6SAytqqJ+wNFkQHRLNhIaIvBKTGhkICNbUUL9w6NIhjI0cq3QYRESdYlIjE3aUJH93pv4MEvVcaZuIvBeTGhkIwfYn8n8n6k5g2IBhSodBRHRDTGqIqEtlNWUYHjZc6TCIiG6qV0nNCy+8AEmSkJOT49r3+uuvIz09HQaDAZIkoa6urstycnNzMWXKFOj1ephMJsyfPx9lZWUdjnGn3L7CId3k7842nEWCIUHpMIiIbsrtpKawsBArV65ESkpKh/3Nzc3IysrC0qVLu11Wfn4+srOzsWvXLmzcuBF2ux2ZmZloamrqVbl9hR2FyZ8duHgA46LGKR0GEVGX3BqX2djYiAceeABvvPEGli1b1uG5K7U2eXl53S5vw4YNHR6/8847MJlMKCoqQlpamtvl9iV2FCZ/JIRAdXM1xkeNVzoUIqIuuVVTk52djXnz5mH27NlyxwMAsFqtAIDwcPcXx7PZbKivr++weQo7CpO/2nthLybHTFY6DCKibulxTc3q1auxb98+FBYWeiIeOJ1O5OTkIDU1FWPHuj8fRm5uLp577jkZI7s5Nj+Rv3E4HWhoa4AxwKh0KERE3dKjmhqz2YzFixfjgw8+QGBgoEcCys7ORmlpKVavXt2rcpYsWQKr1erazGazTBES9Q+7K3fj9tjblQ6DiKjbelRTU1RUhKqqKkyaNMm1z+FwoKCgAK+++ipsNhvUarXbwSxcuBDr169HQUEB4uPj3S4HAAICAhAQENCrMnqCfWrIn9gddtiddgRrg5UOhYio23qU1GRkZKCkpKTDvocffhgjR47E008/7XZCI4TAokWLsGbNGuTl5SE5OdmtcpTCId3kb3ZW7sTUuKlKh0FE1CM9Smr0ev11/VxCQkIQERHh2m+xWGCxWHDixAkAQElJCfR6PRITE10dfzMyMrBgwQIsXLgQwOUmp1WrVmHt2rXQ6/WwWCwAAKPRiKCgoG6XqxR2FCZ/0mxvhlpSQ6fWKR0KEVGPyD6j8IoVKzBx4kQ8+uijAIC0tDRMnDgR69atcx1TXl6O6upq1+Ply5fDarUiPT0dsbGxru3DDz/sUblKYkdh8hd7LHvYl4aIfJIk+kk1Q319PYxGI6xWKwwGg6xlH7l0BKZgEyKCImQtl0gJBWcLkBafpnQYREQAevb5zbWfiMjlXOM5xIXEKR0GEZFbmNQQkUt5XTmGhA1ROgwiIrcwqSGiDjg9ARH5KiY1RAQAsNqsMOjk7W9GRNSXmNTIgPPUkD84ePEgUqJSlA6DiMhtTGpkwip78nUCAiqJbwlE5Lv4DkZEsDRZEB0crXQYRES9wqSGiHCs9hiGDxiudBhERL3CpIaIALAJlYh8H5Maon7uRO0JDDYOVjoMIqJeY1JD1M+dbzqPeH280mEQEfUakxoZcEg3+SqncCodAhGRbJjUyISrdJMvOnjxIMZFjlM6DCIiWTCpIerHaltrMSBwgNJhEBHJgkkNUT910noSScYkpcMgIpINkxqifuqM9QySjclKh0FEJBsmNUT9UHVLNZudiMjvMKmRAwc/kY85cPEAxkeNVzoMIiJZMamRCUc/ka+wNFkQExzDGYSJyO8wqSHqZw5VH8KYyDFKh0FEJDsmNUT9SEV9BWcPJiK/xaSGqB85XnscI8JHKB0GEZFHMKkh6ifK68oxOIwLVxKR/9IoHQCRNxFCuNbyuvJ319peAh0eu54X3wx/u/LYdcy1z19T5tXHXltmZ/s7O8cN4/r6OIdwwOF04EzDGWQlZcl5u4iIvAqTGhmEB4aj+GIxtCqt0qGQDCRIuDKYTYIESZJco9uuPHYdB7ie72y/6zXXPi/hujKvHkHnKvOac3SI65oyVVBdfl76pky1pIZKUkEtqTEkbIgnbhcRkddgUiOD2NBYxIbGKh0GERFRv8Y+NUREROQXmNQQERGRX2BSQ0RERH6BSQ0RERH5BSY1RERE5BeY1BAREZFfYFJDREREfoFJDREREfmFXiU1L7zwAiRJQk5Ojmvf66+/jvT0dBgMBkiShLq6ui7Lyc3NxZQpU6DX62EymTB//nyUlZV1OKa1tRXZ2dmIiIhAaGgo7r33Xly4cKE34RMREZEfcTupKSwsxMqVK5GSktJhf3NzM7KysrB06dJul5Wfn4/s7Gzs2rULGzduhN1uR2ZmJpqamlzHPPnkk/j3v/+Njz/+GPn5+Th//jy+/e1vuxs+ERER+RlJXL3aXjc1NjZi0qRJeO2117Bs2TJMmDABf/7znzsck5eXh5kzZ6K2thZhYWE9Kv/ixYswmUzIz89HWloarFYroqKisGrVKtx3330AgKNHj2LUqFHYuXMnbr/99i7LrK+vh9FohNVqhcFg6FE8REREpIyefH67VVOTnZ2NefPmYfbs2W4F2BWr1QoACA8PBwAUFRXBbrd3ON/IkSORmJiInTt3dlqGzWZDfX19h42IiIj8V48XtFy9ejX27duHwsJCT8QDp9OJnJwcpKamYuzYsQAAi8UCnU53XY1PdHQ0LBZLp+Xk5ubiueee80iMRERE5H16VFNjNpuxePFifPDBBwgMDPRIQNnZ2SgtLcXq1at7Vc6SJUtgtVpdm9lslilCIiIi8kY9qqkpKipCVVUVJk2a5NrncDhQUFCAV199FTabDWq12u1gFi5ciPXr16OgoADx8fGu/TExMWhra0NdXV2H2poLFy4gJiam07ICAgIQEBDgenyl6xCboYiIiHzHlc/t7nQB7lFSk5GRgZKSkg77Hn74YYwcORJPP/202wmNEAKLFi3CmjVrkJeXh+Tk5A7P33LLLdBqtdi0aRPuvfdeAEBZWRkqKiowderUbp2joaEBAJCQkOBWjERERKSchoYGGI3Gmx7To6RGr9e7+rlcERISgoiIiA79XywWC06cOAEAKCkpgV6vR2Jioqvjb0ZGBhYsWICFCxcCuNzktGrVKqxduxZ6vd7VT8ZoNCIoKAhGoxGPPPIInnrqKYSHh8NgMGDRokWYOnVqt0Y+AUBcXBzMZjP0ej0kSerJZSuuvr4eCQkJMJvNHLnVQ7x37uO9cx/vXe/w/rnPH++dEAINDQ2Ii4vr8tgedxTuyooVKzp00E1LSwMAvP3223jooYcAAOXl5aiurnYds3z5cgBAenp6h7Kufs1LL70ElUqFe++9FzabDXPmzMFrr73W7bhUKlWHJi1fZDAY/OaHtK/x3rmP9859vHe9w/vnPn+7d13V0Fzh1jw11Lc4x477eO/cx3vnPt673uH9c19/v3dc+4mIiIj8ApMaHxAQEIDf/OY3HUZzUffw3rmP9859vHe9w/vnvv5+79j8RERERH6BNTVERETkF5jUEBERkV9gUkNERER+gUkNERER+QUmNUREROQXmNT0sWPHjuGee+5BZGQkDAYDpk+fji1btnQ4pqKiAvPmzUNwcDBMJhN++ctfor29/abl7tu3D3feeSfCwsIQERGBxx57DI2Njb0u19t46v51p9zCwkJkZGQgLCwMAwYMwJw5c3DgwAHZr9FTlLx3APDOO+8gJSUFgYGBMJlMyM7OlvX6PEnpewcAly5dQnx8PCRJQl1dnVyX5nFK3bsDBw7gv/7rv5CQkICgoCCMGjUKL7/8skeu0ZOU/Nnzyc8MQX1q2LBh4q677hIHDhwQx44dEz/96U9FcHCwqKysFEII0d7eLsaOHStmz54t9u/fLz777DMRGRkplixZcsMyz507JwYMGCCeeOIJcfToUbFnzx4xbdo0ce+997qOcadcb+SJ+9edchsaGkR4eLh46KGHxNGjR0Vpaam49957RXR0tGhra/P4dctBqXsnhBAvvviiiIuLEx988IE4ceKEOHDggFi7dq1Hr1dOSt67K+655x4xd+5cAUDU1tZ64jI9Qql79+abb4qf/exnIi8vT5SXl4v3339fBAUFiVdeecXj1ywnpe6fr35mMKnpQxcvXhQAREFBgWtffX29ACA2btwohBDis88+EyqVSlgsFtcxy5cvFwaDQdhstk7LXblypTCZTMLhcLj2HTx4UAAQx48fd7tcb+Op+9edcgsLCwUAUVFR4Trm2nvszZS8dzU1NSIoKEh89dVXnrg0j1Py3l3x2muviRkzZohNmzb5VFLjDffuaj/96U/FzJkze3tZfUbJ++ernxlsfupDERERGDFiBN577z00NTWhvb0dK1euhMlkwi233AIA2LlzJ8aNG4fo6GjX6+bMmYP6+nocOnSo03JtNht0Oh1Uqm/+OYOCggAA27Ztc7tcb+Op+9edckeMGIGIiAi8+eabaGtrQ0tLC958802MGjUKSUlJHr/23lLy3m3cuBFOpxPnzp3DqFGjEB8fj/vvvx9ms9nzFy4DJe8dABw+fBj/8z//g/fee6/D77gvUPreXctqtSI8PFzei/QgJe+fz35mKJ1V9Tdms1nccsstQpIkoVarRWxsrNi3b5/r+UcffVRkZmZ2eE1TU5MAID777LNOyywtLRUajUb84Q9/EDabTdTU1Ih7771XABC///3v3S7XG3ni/nWnXCGEKCkpEUOGDBEqlUqoVCoxYsQIcfr0aXkv0IOUune5ublCq9WKESNGiA0bNoidO3eKjIwMMWLECK/+xnc1pe5da2urSElJEe+//74QQogtW7b4VE2NEMr+zl5t+/btQqPRiC+++KL3F9WHlLp/vvqZ4Vtpv5d65plnIEnSTbejR49CCIHs7GyYTCZs3boVe/bswfz583H33XejsrLS7fOPGTMG7777Ll588UUEBwcjJiYGycnJiI6O9olvdkrfv+6U29LSgkceeQSpqanYtWsXtm/fjrFjx2LevHloaWmR61b0mC/cO6fTCbvdjr/85S+YM2cObr/9dvzjH//A8ePHb9gpti/4wr1bsmQJRo0ahQcffFCuy5aFL9y7q5WWluKee+7Bb37zG2RmZvbm0mXha/fPpyiUTPmVqqoqceTIkZtuNptNfPXVV0KlUgmr1drh9UOHDhW5ublCCCGeffZZMX78+A7Pnzx5UgC46beQKywWi2hoaBCNjY1CpVKJjz76SJZyPUnp+9edcv/2t79d12/JZrOJ4OBg8Y9//KO3t8BtvnDv3nrrLQFAmM3mDseYTCbx+uuv9+bye8UX7t348eOFSqUSarVaqNVqoVKpBAChVqvFr3/9a5nuRM/5wr274tChQ8JkMomlS5f28qrl4wv3z5s/M25Go1Qy5U+ioqIQFRXV5XHNzc0AcF3tiUqlgtPpBABMnToVzz//PKqqqmAymQBc7pNgMBgwevToLs9xpf3zrbfeQmBgIO68805ZyvUkpe9fd8ptbm6GSqWCJEkdnpckyXWMEnzh3qWmpgIAysrKEB8fDwCoqalBdXU1Bg0a1K3r9ARfuHf//Oc/O9QEFhYW4kc/+hG2bt2KIUOGdOcyPcIX7h0AHDp0CLNmzcIPf/hDPP/88928Os/zhfvnzZ8ZN6V0VtWfXLx4UURERIhvf/vbori4WJSVlYlf/OIXQqvViuLiYiHEN8PoMjMzRXFxsdiwYYOIiorqMIxu9+7dYsSIEeLs2bOufa+88oooKioSZWVl4tVXXxVBQUHi5Zdfdj3fnXK9nafuX3fKPXLkiAgICBA/+clPxOHDh0Vpaal48MEHhdFoFOfPn+/7m9FDSt47IS4PRx4zZozYvn27KCkpEf/xH/8hRo8e7RPD4ZW+d1fztT41St67kpISERUVJR588EFRWVnp2qqqqvr+RrhJyfvnq58ZTGr6WGFhocjMzBTh4eFCr9eL22+//bpOV6dPnxZz584VQUFBIjIyUvz85z8Xdrvd9fyVN7ZTp0659n3/+98X4eHhQqfTiZSUFPHee+9dd+6uyvUFnrp/3Sn3yy+/FKmpqcJoNIoBAwaIWbNmiZ07d3r0euWk5L2zWq3iRz/6kQgLCxPh4eFiwYIFHYbHezsl793VfC2pEUK5e/eb3/xGALhuGzRokKcvWVZK/uz54meGJIQQilQREREREcnI+4fGEBEREXUDkxoiIiLyC0xqiIiIyC8wqSEiIiK/wKSGiIiI/AKTGiIiIvILTGqIiIjILzCpISIiIr/ApIaIiIj8ApMaIiIi8gtMaoiIiMgv/P9KZN22YqphzAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sandwichSurrounds = list()\n",
    "for u in surrounded:\n",
    "    if u in surrounders:\n",
    "        sandwichSurrounds.append(u)\n",
    "print(sandwichSurrounds,\"are both surrounders and surrounded\")\n",
    "for u in sandwichSurrounds:\n",
    "    plotPoly(unitGeom[u])\n",
    "    plotCenter(u,unitGeom[u])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "4d708ece-cdf8-4de8-9079-70cade1bb117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\n",
      "406 is both a surrounder and surrounded.  Handle this one later\n",
      "886 is both a surrounder and surrounded.  Handle this one later\n",
      "1234 is both a surrounder and surrounded.  Handle this one later\n",
      "now working on sandwichSurround 406\n",
      "now working on sandwichSurround 886\n",
      "now working on sandwichSurround 1234\n",
      "now update unitPops and CPs\n",
      "statePop, updated sum unitPops now 11799448 11799448.0\n",
      "total assigned BGs = 9466 + 6 vs 9472\n"
     ]
    }
   ],
   "source": [
    "print(\"now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\")\n",
    "for i, u in enumerate(surrounded):\n",
    "    uu = surrounders[i]\n",
    "    if u in sandwichSurrounds:\n",
    "        print(u,\"is both a surrounder and surrounded.  Handle this one later\")\n",
    "    else:\n",
    "        unitNbrs[uu].remove(u)\n",
    "        unitNbrs[u] = list()\n",
    "        unitTractList[uu] += unitTractList[u]\n",
    "        for t in unitTractList[u]:\n",
    "            unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "            tractUnitNo[t] = uu\n",
    "        unitTractList[u] = list()\n",
    "for u in sandwichSurrounds:\n",
    "    print(\"now working on sandwichSurround\",u)\n",
    "    uu = surrounders[surrounded.index(u)]\n",
    "    unitNbrs[uu].remove(u)\n",
    "    unitNbrs[u] = list()\n",
    "    unitTractList[uu] += unitTractList[u]\n",
    "    for t in unitTractList[u]:\n",
    "        unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "        tractUnitNo[t] = uu\n",
    "    unitTractList[u] = list()\n",
    "print(\"now update unitPops and CPs\")\n",
    "unitPop = [np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits) ]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits) ]\n",
    "print(\"statePop, updated sum unitPops now\",np.sum(tractPop),np.sum(unitPop))\n",
    "print(\"total assigned BGs =\",np.sum([len(unitTractList[u]) for u in range(nUnits)]),\"+\",len(skipList),\"vs\",nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b12ab17f-52f4-4173-a207-20fddb86c487",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "07f4b7f3-6cbc-4aeb-93a2-80c8a9abc494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\n",
      "To be safe, we will reassign unit nbrs from bg topology in the next code block\n",
      "state pop = 11799448 = 11799448\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\")\n",
    "print(\"To be safe, we will reassign unit nbrs from bg topology in the next code block\")\n",
    "oldPop, oldGeom, oldList = unitPop.copy(), unitGeom.copy(), [unitTractList[u].copy() for u in range(nUnits)]\n",
    "oldNbrs = [unitNbrs[u].copy() for u in range(nUnits) ]\n",
    "\n",
    "unitPop, unitGeom, unitTractList, oldUnitNo, nOldUnits = list(), list(), list(), list(), nUnits\n",
    "for u in range(nOldUnits):\n",
    "    if u not in surrounded:\n",
    "        oldUnitNo.append(u)\n",
    "        unitPop.append(np.sum([tractPop[t] for t in oldList[u] ]) )\n",
    "        unitGeom.append(oldGeom[u])\n",
    "        unitTractList.append(oldList[u].copy())\n",
    "nUnits = len(unitPop)\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "#for u in range(nUnits):   #to be safe, see next block for bg topology approach\n",
    "#    unitNbrs[u] = [oldUnitNo.index(uu) for uu in oldNbrs[oldUnitNo[u]] ]\n",
    "    \n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3128ef14-48bb-46d9-9c75-20c82b802cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did we create any bigUs via surround?\n",
      "1293 1.662\n",
      "1773 1.066\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"did we create any bigUs via surround?\")\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > 1.05 * aDP:\n",
    "        print(u,r3(unitPop[u]/aDP))\n",
    "        plotPoly(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "4d17d6dd-d9cf-463b-b5c9-4ba101774da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, we will completely split these into bg1 units rather than recovering their surroundees\n",
      "splitting surrounding + surrounded unit 1293 with pop/aDP 1.6618262989929697\n",
      "splitting surrounding + surrounded unit 1773 with pop/aDP 1.066456074894351\n",
      "we now have 3462 total units.  Update the reverse tractUnit assignments\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, we will completely split these into bg1 units rather than recovering their surroundees\")\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > 1.05 * aDP:\n",
    "        print(\"splitting surrounding + surrounded unit\",u,\"with pop/aDP\",unitPop[u]/aDP)\n",
    "        for i,v in enumerate(unitTractList[u].copy()) :\n",
    "            if i == 0:\n",
    "                unitPop[u] = tractPop[v]\n",
    "                unitGeom[u] = tractGeom[v]\n",
    "                unitTractList[u] = [v]\n",
    "                unitCP[u] = tractCP[v]\n",
    "            else:\n",
    "                unitPop.append(tractPop[v])\n",
    "                unitGeom.append(tractGeom[v])\n",
    "                unitTractList.append([v])\n",
    "                unitCP.append(tractCP[v])\n",
    "nUnits = len(unitPop)\n",
    "print(\"we now have\",nUnits,\"total units.  Update the reverse tractUnit assignments\")\n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "8f87a4ea-c22e-4599-b029-84dea7369014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check for discontig units\n",
      "we found a total of 0 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "print(\"check for discontig units\")\n",
    "isContigUnit = [False]*nUnits\n",
    "nonContigUnits = list()\n",
    "for c in range(nUnits):\n",
    "    isContigUnit[c] = isContiguous(unitTractList[c],bgNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d8ea0c58-fa3f-4eaf-a4e0-fc209d5433f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reassign unit nbrs based on bg topology\n",
      "build unit nbr list based on adjacent bg's\n"
     ]
    }
   ],
   "source": [
    "print(\"reassign unit nbrs based on bg topology\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent bg's\")  #these are list-based, not geom-based\n",
    "for bg in range(nTracts):\n",
    "    u = tractUnitNo[bg]\n",
    "    for bb in bgNbrs[bg]:\n",
    "        uu = tractUnitNo[bb]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "fb97611f-2fe5-4a8f-8478-4c6955689668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining border counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use previously defined and read-in border bgs\n"
     ]
    }
   ],
   "source": [
    "print(\"defining border counties\")\n",
    "Kelley, Knbrs = 5879, [5870, 5871]  #in case we used previously saved bg topology vs. defining in this run\n",
    "borderCounties = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(mainMAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "usePrevious = True\n",
    "if usePrevious:\n",
    "    print(\"We will use previously defined and read-in border bgs\")\n",
    "else:\n",
    "    print(\"Now we determine and plot the border bgs\")\n",
    "    borderBGs = list()\n",
    "    for c in borderCounties:\n",
    "        for bg in countyTractList[c]:\n",
    "            if isRookAdj(tractGeom[bg],mainMAP.exterior) or bg == Kelley:\n",
    "                borderBGs.append(bg)\n",
    "                plotPoly(tractGeom[bg])\n",
    "    plt.show()\n",
    "    isBorderBG = [0]*nTracts\n",
    "    for bg in borderBGs:\n",
    "        isBorderBG[bg] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "8bcc5cfe-d24f-45bf-b006-778d3147e509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check -- Do most bgs have 2+ neighbors?\n",
      "46 in county 37 has only neigborlist [54]\n",
      "520 in county 63 has only neigborlist [522]\n",
      "1098 in county 16 has only neigborlist [1089]\n",
      "1420 in county 53 has only neigborlist [7933]\n",
      "2511 in county 25 has only neigborlist [2450]\n",
      "2985 in county 80 has only neigborlist [2745]\n",
      "4101 in county 79 has only neigborlist [4102]\n",
      "4921 in county 7 has only neigborlist [4947]\n",
      "6277 in county 66 has only neigborlist [6718]\n",
      "6329 in county 49 has only neigborlist [1150]\n",
      "6369 in county 28 has only neigborlist [3717]\n",
      "6601 in county 28 has only neigborlist [6424]\n",
      "7275 in county 35 has only neigborlist [7286]\n",
      "7420 in county 23 has only neigborlist [7413]\n",
      "7696 in county 24 has only neigborlist [1384]\n",
      "8531 in county 85 has only neigborlist [8914]\n",
      "8694 in county 19 has only neigborlist [6949]\n",
      "9025 in county 28 has only neigborlist [6702]\n",
      "9277 in county 24 has only neigborlist [3069]\n"
     ]
    }
   ],
   "source": [
    "print(\"check -- Do most bgs have 2+ neighbors?\")\n",
    "for b in range(nTracts):\n",
    "    if b in skipList:\n",
    "        if len(bgNbrs[b]) > 0:\n",
    "            print(\"warning: skipped bg\",b,\"has some bg neighbors\")\n",
    "    else:\n",
    "        if len(set(skipList).intersection(set(bgNbrs[b])) ) > 0:\n",
    "            print(\"warning. b\",b,\"has skipList neighbors\")\n",
    "        if len(bgNbrs[b]) < 2:\n",
    "            print(b,\"in county\",countyNo[b],\"has only neigborlist\",bgNbrs[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "3eeda00e-460c-4ca0-9d79-bbf89e24c4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "how contig is the set of border BGs?\n",
      "the contig list has length 367 vs total 367\n",
      "border BG 5879 has < 2 border neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"how contig is the set of border BGs?\")\n",
    "contigList = getContigFromStarter(borderBGs[0],borderBGs,bgNbrs)\n",
    "print(\"the contig list has length\",len(contigList),\"vs total\",len(borderBGs))\n",
    "for b in borderBGs:\n",
    "    if len(set(bgNbrs[b]).intersection(set(borderBGs))) < 2:\n",
    "        print(\"border BG\",b,\"has < 2 border neighbors\")\n",
    "        plotPoly(tractGeom[b])\n",
    "        plotCenter(b,tractGeom[b])\n",
    "        for bb in bgNbrs[b]:\n",
    "            plotPoly(tractGeom[bb],0.3)\n",
    "            if bb in borderBGs:\n",
    "                plotCenter(\"border\"+str(bb),tractGeom[bb])\n",
    "            else:\n",
    "                plotCenter(\"nonb\"+str(bb),tractGeom[bb])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "09b51578-0314-4c13-a1f3-196ca8f55bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating some corner connectivity\n"
     ]
    }
   ],
   "source": [
    "#THIS BLOCK ONLY NECESSARY IF WE DID NOT READ IN THE modBGtopology\n",
    "#print(\"creating some corner connectivity\")\n",
    "#bgNbrs[7664].append(6683)\n",
    "#bgNbrs[6683].append(7664) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a6a8cdd3-55e2-4f24-a63a-e0efe0a9fb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Border bg integrity check: number of border BGs = 367\n",
      "border is discontig if we skip bg 5870\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Border bg integrity check: number of border BGs =\",len(borderBGs))\n",
    "nLoop = 0\n",
    "for b in borderBGs:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderBGs).difference({b})),bgNbrs)\n",
    "    if not isUnbroken:\n",
    "        nLoop +=1\n",
    "        print(\"border is discontig if we skip bg\",b)\n",
    "        if nLoop < 5:  #avoid excessive printing\n",
    "            for bb in smallPieceList:\n",
    "                plotPoly(tractGeom[bb],0.2)\n",
    "                plotCenter(\"n\"+str(bb),tractGeom[bb])\n",
    "            plotCenter(b,tractGeom[b])\n",
    "            plotPoly(tractGeom[b],1)\n",
    "            plt.show()  #below is OK - Kelleys Island is part of a larger unit\n",
    "borderBGset = set(borderBGs) "
   ]
  },
  {
   "cell_type": "raw",
   "id": "42d9812d-9191-434a-8064-9b95ea518bf7",
   "metadata": {},
   "source": [
    "date = \"17Nov24\"  #THIS WAS DONE IN OH_615COIsnap99, so do not repeat here\n",
    "isBorderBG = [0]*nTracts\n",
    "for b in borderBGs:\n",
    "    isBorderBG[b] = 1\n",
    "tractCPx, tractCPy = [tractCP[t].x for t in range(nTracts)], [tractCP[t].y for t in range(nTracts)]\n",
    "print(\"lets write the modified blockgroup topology to a file\")\n",
    "bgNo = [b for b in range(nTracts)]\n",
    "bgDF = pd.DataFrame( {\"bgNo\":bgNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"bgPop\":tractPop,\"bgNbrs\":bgNbrs,\n",
    "                     \"countyNo\":countyNo,\"isBorderBG\":isBorderBG} )\n",
    "outname = STATE+str(nDistricts)+\"mod_bgTopologies_\"+date+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "bgDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e934066e-a4d6-4279-afad-1ac87da4b4c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a histogram of number of unit neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3217 has only neighbor 3214\n",
      "unit 3442 has only neighbor 3434\n",
      "unit 3460 has only neighbor 3429\n"
     ]
    }
   ],
   "source": [
    "nUnitNbrs = [len(unitNbrs[u]) for u in range(nUnits)]\n",
    "print(\"here is a histogram of number of unit neighbors\")\n",
    "plt.hist(nUnitNbrs,bins=[0,1,2,3,5,7,10,20,30,40,80])\n",
    "plt.axvline(0.5,ls=\"--\")\n",
    "plt.axvline(1.5,ls=\"--\")\n",
    "plt.show()\n",
    "for u in range(nUnits):\n",
    "    if nUnitNbrs[u] == 1:\n",
    "        print(\"unit\",u,\"has only neighbor\",unitNbrs[u][0])\n",
    "    if nUnitNbrs[u] == 0:\n",
    "        print(\"unit\",u,\"has NO NEIGHBORS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "89da825f-df3a-4474-a052-78d55fca85f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "6a57ddc1-0e5a-43e9-b171-879ffa97bb4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new surrounded = [542, 2545, 2546]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "surrounded3 = list()\n",
    "for u in range(nUnits):\n",
    "    if unitCP[u].distance(Point(-81.43607160636685, 40.49556368047748) ) < 0.04:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u],6)\n",
    "        if len(unitNbrs[u]) < 4:\n",
    "            surrounded3.append(u)\n",
    "print(\"new surrounded =\",surrounded3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d92ecea6-b4f8-4313-9205-089bc899a590",
   "metadata": {},
   "outputs": [],
   "source": [
    "stillSurrounded = [3217, 3442, 3460]\n",
    "stillSurrounders = [unitNbrs[u][0] for u in stillSurrounded]\n",
    "for u in surrounded3:\n",
    "    stillSurrounded.append(u)\n",
    "    stillSurrounders.append(1766)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "0502a1b8-ae55-4729-a678-3a0ef5770b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 3217 and its surrounder 3214\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 3442 and its surrounder 3434\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 3460 and its surrounder 3429\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 542 and its surrounder 1766\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 2545 and its surrounder 1766\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing surrounded u 2546 and its surrounder 1766\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,u in enumerate(stillSurrounded):\n",
    "    uu = stillSurrounders[i]\n",
    "    print(\"showing surrounded u\",u,\"and its surrounder\",uu)\n",
    "    plotPoly(unitGeom[u],0.6)\n",
    "    plotCenter(u,unitGeom[u])\n",
    "    plotPoly(unitGeom[uu])\n",
    "    plotCenter(\"big\"+str(uu),unitGeom[uu],8)\n",
    "    for uuu in unitNbrs[uu]:\n",
    "        if uuu != u:\n",
    "            plotPoly(unitGeom[uuu],0.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b18e8ef7-ffc5-4299-ab09-3976ecb5344e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not sure why we missed these dudes.  Treat as surrounded\n",
      "now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\n",
      "now update unitPops and CPs\n",
      "statePop, updated sum unitPops now 11799448 11799448.0\n",
      "total assigned BGs = 9466 + 6 vs 9472\n",
      "Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\n",
      "To be safe, we will reassign unit nbrs from bg topology in the next code block\n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on bg topology\n",
      "build unit nbr list based on adjacent bg's\n"
     ]
    }
   ],
   "source": [
    "print(\"not sure why we missed these dudes.  Treat as surrounded\")\n",
    "surrounded2 = stillSurrounded.copy()\n",
    "surrounders2 = stillSurrounders.copy()\n",
    "#sandwichSurrounds = list()\n",
    "print(\"now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\")\n",
    "for i, u in enumerate(surrounded2):\n",
    "    uu = surrounders2[i]\n",
    "    if u in surrounders2:\n",
    "        print(u,\"is both a surrounder and surrounded.  Handle this one later\")\n",
    "        sandwichSurrounds.append(u)\n",
    "    else:\n",
    "        unitNbrs[uu].remove(u)\n",
    "        unitNbrs[u] = list()\n",
    "        unitTractList[uu] += unitTractList[u]\n",
    "        for t in unitTractList[u]:\n",
    "            unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "            tractUnitNo[t] = uu\n",
    "        unitTractList[u] = list()\n",
    "print(\"now update unitPops and CPs\")\n",
    "unitPop = [np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits) ]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits) ]\n",
    "print(\"statePop, updated sum unitPops now\",np.sum(tractPop),np.sum(unitPop))\n",
    "print(\"total assigned BGs =\",np.sum([len(unitTractList[u]) for u in range(nUnits)]),\"+\",len(skipList),\"vs\",nTracts)\n",
    "\n",
    "print(\"Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\")\n",
    "print(\"To be safe, we will reassign unit nbrs from bg topology in the next code block\")\n",
    "oldPop, oldGeom, oldList = unitPop.copy(), unitGeom.copy(), [unitTractList[u].copy() for u in range(nUnits)]\n",
    "oldNbrs = [unitNbrs[u].copy() for u in range(nUnits) ]\n",
    "\n",
    "unitPop, unitGeom, unitTractList, oldUnitNo, nOldUnits = list(), list(), list(), list(), nUnits\n",
    "for u in range(nOldUnits):\n",
    "    if u not in surrounded2:\n",
    "        oldUnitNo.append(u)\n",
    "        unitPop.append(np.sum([tractPop[t] for t in oldList[u] ]) )\n",
    "        unitGeom.append(oldGeom[u])\n",
    "        unitTractList.append(oldList[u].copy())\n",
    "nUnits = len(unitPop)\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "#for u in range(nUnits):   #to be safe, see next block for bg topology approach\n",
    "#    unitNbrs[u] = [oldUnitNo.index(uu) for uu in oldNbrs[oldUnitNo[u]] ]\n",
    "    \n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "\n",
    "print(\"reassign unit nbrs based on bg topology\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent bg's\")  #these are list-based, not geom-based\n",
    "for bg in range(nTracts):\n",
    "    u = tractUnitNo[bg]\n",
    "    for bb in bgNbrs[bg]:\n",
    "        uu = tractUnitNo[bb]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "70be847a-2968-4e64-aa59-589233283de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check again for units with 0 or 1 neighbors, then confirm unit contiguity\n",
      "here is a histogram of number of unit neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"check again for units with 0 or 1 neighbors, then confirm unit contiguity\")\n",
    "nUnitNbrs = [len(unitNbrs[u]) for u in range(nUnits)]\n",
    "print(\"here is a histogram of number of unit neighbors\")\n",
    "plt.hist(nUnitNbrs,bins=[0,1,2,3,5,7,10,20,30,40,80])\n",
    "plt.axvline(0.5,ls=\"--\")\n",
    "plt.axvline(1.5,ls=\"--\")\n",
    "plt.show()\n",
    "for u in range(nUnits):\n",
    "    if nUnitNbrs[u] == 1:\n",
    "        print(\"unit\",u,\"has only neighbor\",unitNbrs[u][0])\n",
    "    if nUnitNbrs[u] == 0:\n",
    "        print(\"unit\",u,\"has NO NEIGHBORS\")\n",
    "for u in range(nUnits):\n",
    "    contig = isContiguous(unitTractList[u],bgNbrs)[0]\n",
    "    if not contig:\n",
    "        print(u,\"is a noncontig unit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5e97fc0b-1757-49c4-a723-7f594bb264af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop = 11799448 = 11799448\n",
      "sum of unit areas = 11.460253084569999 vs map area 11.46025308457\n"
     ]
    }
   ],
   "source": [
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "print(\"sum of unit areas =\",np.sum([unitGeom[u].area for u in range(nUnits)]),\"vs map area\",mainMAP.area + tractArea[Kelley])\n",
    "#main excludes Kelleys Island"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "1cb139b5-d99d-4693-a8ef-e9173fb475ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state nTracts = 9472 = 9466 + 6\n"
     ]
    }
   ],
   "source": [
    "print(\"state nTracts =\",nTracts,\"=\",np.sum([len( unitTractList[u] ) for u in range(nUnits)]) ,\"+\",len(skipList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "930e18b7-dc45-456a-83da-6682553baff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spot check of new unit nbr lists ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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4xme9IEmScMPCKXj6Cx9BUiSx/BdPYdO+k+PyXERj5Y/74bF49C6D8hADD1GuqAnANLrBwWqkBxZnSZYL6m/mBDeevvUqvHdyDT7/x6dx31/fhqqNrkWKaLz4436U2Mb3vwUqTAw8RLkQah/VYOVekVgcbsf4z53jtpnx6xsuxpc//AH84rVXcONvX0Z3ODHuz0s0UprQ9C6B8hQDD1EuRLsB1+ivKgnEk/BmedDyYCRJwm2XzMJD/7Yce1ubcNX9z2DPCX9OnptoKN2xbvisPr3LoDzFwEM03sIdgLN8TLsIxFLwOXITeHpdPLMcT996LXx2C6779ZN4bAdXayd99cR74LP59C6D8hQDD9F4i3YDjtJRPzyWVJFIaTlr4TlTTYkD/+/zl+PKObPw1Sf/im888eap5SyIcssf98NtcetdBuUxBh6i8aSpGa+b9W6BaBIAct7C08tmVvD/fey9+NYVi7H+n9vx8V+9gLZgTJdaqHh1xbpQbh9bSykVNwYeovHUdRgomTqmXfijSQhIurTw9JIkCTcunII/fPpaHOvpxFU/fxo7jnbrVg8Vl0AiAJfZpXcZlOcYeIjGlQDksf1n1hNNQoLQNfD0umByKZ75P8swyevBJx/8Mx5+/SiE4KXrNL46oh2ocGQ+QznRmRh4iMZLqB1wlI15N/5IukvLa7eMeV/ZUOmx4U+fXYyPnnce/uuZTfjqozsRS3JJChofwUQQbjPH7tDYMfAQjZdYz5gGK/cyQpfWu1lMMu5ZcT6+d83leGrfHnx03SY0dUX0LosKUHu0na07lBUMPETjQU2NebByr55oEnaTDIvJeP+5fuw9tXj0lmvRFY3gip8/hvX/OMYuLsqahJqARTZGyyblP+OdQYkKQfdhoHRaVnbljybhsZuysq/xcG6NFxv/4xosnT0HX/vLJvzrb7awtYeyoiXcgkmuSXqXQQWCgYdovEhSVnYTiCbhsho38ADpJSl+8LH34P/+6zU40tmNJT97FL96qREpztlDYyBBgpSl/46IGHiIsi14EnBmb8xBOBSE1e7I2v7G0yX1lfjbymX4+Pzzce/zL2LZAxuxt5nLUhCR/hh4iLItHgDsvqztLhXugikLg59zxWk14VvLzsVjt6xAUtWw/JeP4Z5n9vJKLspIUk3CJBu7ZZPyCwMPUTapSUBWsrrLeDQMuyP/Jl07v64Ez9y+FHd88AN4aNt2XPbTp/DqwQ69y6I80RpuRbWzWu8yqIAw8BBlU/eRMc+s/G6BeAo+Z35eqWJWZNx+6Uw883+uQ6XLhU/97kl8ecN2dIcTepdGBicgOH6HsorthbmWCAPB1vSAVtkEWFyA2QGYrFkb5Eo6y/LvMRRLGmoOntGYXuHChs8uwh+3N+Ge5/6OZ97ahytmz8QHZ9Zi3kQvppQ7YVb49xcRjR8GnlwLtgJl09Pfq8l0AIp0Amq8/3ZCDP3BOdj9kgxYPYDNm/WuFRpGoAVwTcjqLoUQCEZTeR94AECWJXzyvXW4bO5H8b9bj+KpPfvxxO4DAAC7RcaSmXNx00XTcH5dic6Vkt6EEBDgfE6UXQw8elLM6cGtWRzgCk0FYn7A3wSIQS4JVpNARf3pnxOn5kwx29nKNJBIV3rW5OEm1FMTgCe7Yw4iCRUpIeB15GeX1kDKXVasumwWVl02Cz2RBN5uDWLb4U78Ydcu/PnXu3HOhCp86l/mYuYEFxIpge5IAh2hODpCCfgjCZgVGTazAps5/a/VrMBmkuG2meCxmeGxm0/9a4LLaoKJLUd5h+N3aDww8BQaWUkvZzDUVT2djf1/9jelW4WCLdmpwexItzAVQoAKngSECvgm69Ji5o/2rqOV/y08A/E5LLhwWhkunFaG/3PJTGzZ34b/u3Uv1jz1Qr/tzIoEn90Ct82ElCYQT6qIqxriSQ3xlAptiCzqMCtw2UxwWS3wWr0os3tQ7raizGlFqdOCMpel3/clDsuQs1q3BWPY2xxAqz+GlKpBANC0dHuEEIB2Khj3fn/27QKaSN8mkP4evbdBnNo2/T1695H+tu97nPpe1TQktSSSahIJLQmhmVFid2JquRPTK1yYWu5ETYk966FP0wTiKQ2xpIroqa9Y35eGhKrBYVbgtKZD6ASvFVbTyP/7SWgJWJTCCflkDAw8uWaEAPDulgrFnL2WCSGAZCTdIjLaAGV2AFY3YHHqe7wSESAZztqMyaPREynswHMmRZawaM4ELJozAf5IEq2BGMyKhDKnFR67adABrEIIJFWBcDyFQCyJQDSFYCzZ930glkQglkJPJIbOaDc6o6043pJEdySBnmgS8eTZLaEuq4IShxWldi/KHT64rSY0hzrQ2NGJzjMGXJvkUxPjpf8PWZL6/QsJkCQJ0rvux6l/Jen0bX37kQD51PbpiffSzyX37efUv6d+NstmmCQzTLIJshzCgY7jeGx3tO91mRUZ5W4LzLIMWZKgyOljrcgyZAkwySaYJAvMsgWKbIIsJ6AijlgyhVhKRSKVDpaxUyEzlkzfdrbe38/A6bPEaUG5y4Ip3ircs+I9KHdZh3lHEGUXA0+uGWGdIfcEoO1toGRyuhUmmzVJUjqoWJyje3xvYIqHgHDb6GsTIh1U5DH8ZSsrQDIKJGOA2Tb6/YxBbwuPrwgCz5m8DjO8jpG9ZkmSYDFJsJgsKBnF1WyRRAqdoQQ6wwl0hdNdZ13hBDpDcbSGutERacPJHhXVrjJ84vzJOGeiB+dM9GJSiR2KbIA/YAagaQItgRgOtYdwuCOMFn8MqiaQUtOtQilNQBPpn5NqCgktjqRIIKXGIYQFZrhQ6rLAZpJhtyiwn+o6tJ/qSkz/m/6yW5S+7WyntlFkCdGkinA8BX80iRZ/DIc7wnjgxbdxsH0fvuifx8BDOcfAU4ysbqByNtB1OD3IWU3qXdFpYw1MvboOY7C/NEfMZAUmnAN0HDxdm6syffxypNC7tIzAYTHBUWpCbWl+zGY9ErIsYZLPjkk+Oz4wU/+VxrvDCdzyu5dhs/fgfz66DPMmeQfdtjPaCZ/Vl7viqGgw8OhhuCuwcqV0avoqsUKczVRo2RtzUz7j9PdtbwFlM9LdgDngj6a7TzwMPJSnDraF8Jnf/x3+eBf+8Onlw16FF0gEMNWb3bmsiAAGntxTzICWytkH5rDG2pJSbCpmA627gco5Ofkd+qNJOK0mw3adEA3mZCCGr//5H3j+4CFM8rjx+GevxZTyoc83PbEeuMz5N6s45QcGnlxTrEAqbpzAQ5mRJKDqXODknvS/48wfTcJt8JXSid4tpWr43O9fwvFAN+656lJce/4k2MxDt7gmtSQ6Y52Y7pueoyqp2PBMmmuKOT1fC+UvLZWeITsH/OE43OzOojzzs80Hsefkcfzx5mvxninDL3yrCQ2Heg5hVsmsHFRHxYozcuWaYjHWIGHKXPdRoGRKTp4qHu6Bks2JKYnG2fYjXfjZKy/j1vcvHFHYEULgne53MKtkFtfOonHFwJNrioUtPLmQCAH+4+lLyrOp5Z/pS91zdGJWI90wOX05eS6isWrqiuAzj2zEeVVTsHJR/bDbCyGwv3s/ZpbMZNihcccurVxTzIDGFp5xV31eOuxEOtJjpkZCNgGOsqEnPLR5AVdV9uocRjiWQGmFPWfPRzQW3/nLP2EzKfjNjR8YdjHYuBrHEf8RzCqZBVni3940/hh4ck2SjDH5YDEw2wBvzci3V5PphVxDJ4fezt8ElM8cW20jFIylMJVjeCgPtAfj2HzwHXxz6YeGnDRSCIFjwWOQIaO+dPhWIKJsyShWP/DAA2hoaIDH44HH48HChQvx7LPP9t3f2NiIFStWoKKiAh6PB9dffz1Onhzmw+MMa9euhSRJuOOOO866b+vWrbj00kvhdDrh8XjwwQ9+ENFoNJPyiYammAF3VXo1+6G+chR2ACAQTXLSQTK0WFLFnf9vBy7+4R9hN5tw9bmDL1PTGm7FYf9hTHRNRK2nNodVEmUYeGpqarB27Vrs2LED27dvx6WXXorly5dj7969CIfDWLJkCSRJwubNm/HKK68gkUjgmmuugaYNsmr3GbZt24Z169ahoaHhrPu2bt2KK664AkuWLME//vEPbNu2DbfffjvksSwbQGRwmiYQTiThG+ESC0S5Fogl8cU//AOP79mNG97zHvzh5qsGXN6jI9qBQz2H4LF4MM03DWaZ72nKvYy6tK655pp+P3/3u9/FAw88gNdeew0nTpzAkSNH8MYbb8Dj8QAAHnroIZSUlGDz5s1YvHjxoPsNhUL4t3/7N/zqV7/C3Xfffdb9q1atwhe/+EXceeedfbfV17MplApbMJ6CEFxWgoylO5zAPc++ia3HGnHSH4MsS/j5x5Zi8dwJZ20bSATQEelAqa0U03z6LcJLBIzhKi1VVbF+/XqEw2EsXLgQ8XgckiTBaj29IJzNZoMsy3j55ZeH3Ndtt92Gq666asBQ1NbWhtdffx2VlZW46KKLMGHCBHzoQx8adp/xeByBQKDfF1E+8UeKc+FQMq5YUsX1v/4bNh04gMtnnYtvLv0wNq/82FlhJ5aK4VDPIcRSMUzzTYPP5tOnYKIzZDxoeffu3Vi4cCFisRhcLhcef/xxzJ07FxUVFXA6nVi9ejXuueceCCFw5513QlVVtLS0DLq/9evXY+fOndi2bduA9x86dAgA8K1vfQs/+MEPMH/+fPzud7/DokWLsGfPHsycOfB4invvvRff/va3M315RIbhjyYhIHEdLRpX0YSKLfvbEEmoSKoakppAStWQUgUSqoakqsFlNUHVBLYfbUNjZyue/Px1Ay4AqmoqjgaOwqJY2KJDhpNx4Kmvr8euXbvg9/vx6KOP4qabbsKLL76IuXPnYsOGDbj11lvx05/+FLIs45Of/CQWLFgw6FibpqYmrFy5Eps2bYLNZhtwm97xP5///Odx8803AwDOP/98PP/88/jNb36De++9d8DHrVmzBl/60pf6fg4EAqit5SA5yh89pxYOZZcWjYekqmHD9uP40ZbX0BGKAkhPxaDIgCJLMMsyTIoEkywhmlAhyxJ8divuW77krLDTe+WVKlRM9U7lnDpkSBkHHovFghkz0qtHX3DBBdi2bRt+8pOfYN26dViyZAkaGxvR0dEBk8kEn8+HqqoqTJs2cNLfsWMH2trasGDBgr7bVFXFSy+9hJ/97GeIx+Oork6P+J87d26/x86ZMwfHjh0btE6r1dqve40o3/ijp7q0OGiZskgIgWd2t+L7f3sdTT3duHLOXHxp8TmoKXHAJEuQM1yotjXcikgyglp3LcxcI5AMbMzz8Giahni8/8Ru5eXlAIDNmzejra0Ny5YtG/CxixYtwu7du/vddvPNN2P27NlYvXo1FEXBlClTMHHiROzfv7/fdgcOHMDSpUvHWj6RYfmjSSiSBBcXD6UsebWxA999djveamvB+6dOw/2fuATnTDy7a2ok/HE/OqOdmOCcgCpn7ibjJBqtjM6ka9aswdKlS1FXV4dgMIhHHnkEW7ZswcaNGwEADz74IObMmYOKigps3boVK1euxKpVq/pdUbVo0SKsWLECt99+O9xuN+bNm9fvOZxOJ8rKyvpulyQJX/3qV/HNb34T5513HubPn4+HHnoIb7/9Nh599NGxvv7cSyW4UjqNSE8kCZdVYfcAjdnRzjD+++ldeKHxAOZWVeGRTy/DhdPKRr2/5lAzZEnmOB3KKxkFnra2Ntx4441oaWmB1+tFQ0MDNm7ciMsuuwwAsH//fqxZswZdXV2YMmUK7rrrLqxatarfPnq7vDJxxx13IBaLYdWqVejq6sJ5552HTZs2Yfr06RntxxDiQcDq0bsKygOBaBJu+9lzmhCNVDyl4keb9uPBf/wDPrsDP/rIEiw7b+KYQnRLqAVOsxNe6+hahoj0IglRHOscBAIBeL1e+P3+vnmCdNF9BPDWArKiXw2UF+587E10n9iPdV/8mN6lUB5qC8Tw2d+/hLfbT+DzCxfi1g/PhN0ytvNOQk2gNdyKOk9dlqokGl62Pr85OCDXNJVhh0YkEI7CMcjVi0RD2dXUg88+sgkA8Md/X4H5tb6s7Lcp2ITpvjxsWScCAw+RYaXCPXC72W1AmXlsx3F87ennUV9RjV9/6oOo9GQnNGtCgyLxjzXKXww8RAaVigZgreLVLzQyKVXDd5/Zi4e2v4YV8+bjnhULYDVlL6A0h5pR467J2v6Ico2Bh8iAhBBobA+glHNl0gj0RBK49ZFXsO34YXxjyWLcdNGUrF/dpwkNJpkfGZS/uNw4kUFJEHh0x3Ec7ggjGEuiSK4voAy9ebwHV93/F7x18iR+96ll+PT7OdMx0UAY13OJc/DQCEmShPpZc6Ac2ItP/3A9AMCiyCh1mGF2lcDqrkCZ24pZE9y4ceEUWEz826XYCCHwm5cP43ubX8Ss8mqs//fLUVvqGLfnk8AQRfmNgSeXAicAHy/npJH5xacvwuGO89AaiKEjlEBnKI6OYAwhfxcSwRZ0nYjj8R3tMMlX4tPvn6p3uZRD/kgSX9rwOl44tB83XvAv+NqV8xh6iYbBwJNLQuMl6TRikiRhWoUL0ypcg25z5TcfQkLVclgV6e2NY934P+tfQCgRwy+uvwpLzsnNwHYBdqlSfmPgIcpTSVVDOJGCz8HZmIuBEAK//vshfH/zS5hTOREbPnkFakrGrwuLqNAw8BDlqZ5IejX1kjwIPK3+GPac8MNmVlBbakdNiQNKhqtyF7OeSAKrNryOFw8dwL+/931YfcVcmBV2YRFlgoEnV5JRwGTVuwoqID2RBACgxGH8gfDf2PAmduzvgHrqZ7MiYaLPjupKJyZXuzCt3IlpFS5MLXeizGnhVUZn2HG0C7etfwFRNYFff/JqXDp7gt4lEeUlBp5cCTQDpVxZmLKn+1QLj9G7tCKJFHY3dmH5B6fg0xdNwdGuMA53hHGoLYRjrSG8tLMFf/LH0HvVvcuqoKbMieoqJ6ZVujC1womp5ekvh6V4TlmaJrDupUb8cMvfce6EGvz8kxdjos+uSy1JLQmF4w8pzxXP2cMI+FcrZVG+tPC88HY7oikNN100GXVlDtSVOfCBmRX9toklVRzriuBQewiHOsI4fDKE5tYwdr7dAf+pYAcAFW4rJpY7UFvtxNQK96mWIScm+ewwFVAXTyyp4nO/fxkvHzmIz114Ib6yZI6ur88f96PEWqLb8xNlAwMPUZ7qHcPjtRs78Pz1jWZMr3Zhcplz0G1sZgWzJrgxa4L7rPt6Iol0CGoP41BHCEdOhrDvnW48948TSKTSzUImRcIknx1VlU5MqXJhWoUTU8vT/+ZjF9kvtjTi9WOH8Jt/vQYfrq/UuxxEk1GU2cr0LoNoTBh4ciHUBjjL9a6CCkx3JAGn1WTolo1IIoVtb7fjxstnjHofPocFC+osWFDXv4VB0wRaAjEcag/17yJ7o38XmdOqYEqVG/9z83tQ6jR29x8AHO+O4IFXt+LT732vIcJOr3wLjUTvxsCTC/Eg4DLOiYsKQ3ckCa/N2K07W/a3I57ScNW51VnftyynW3Um+exDdJGF8eKBNjy79Ti6wvG8CDzf+csueGx2fPHSer1LISooxv3TkIiG1BNJwGM39t8sf/1nC6ZOGLo7azz0dpFdMa8K/zKlFAB0G/CbiVcPdmDTgf24a8n74bQa+3dLlG8YeIjyVCgUgM0++CzMeoslVWzf14YPn5/91p1MnOiOwuswG/4Kr5Sq4b+e3oqG6klYPn+i3uUQFRwGnvEWaAbcuZn6nYqLGu6EyWXcgaSvHOxANKlh6Tx93//NPRFM8Bh/Dqzfv3YUh7vbcfey9xluvAyXlaBCwMAz3pJRwJLb5nwqDtFIBG6XcVt4/vpmCyaV2TGjUt8aT7ZHUVZq7O6szlAcP3zhVXy0YT7mTfLqXQ5RQWLgIcpT/ljKsMtKJFUNr+9tw4fmV+neWtHRHUVVubHXnPrexr0AgNWXn6NzJUSFi4FnPAk2A9P4EEIgEEmi1GnMq7T+cbgLgWgKV8zTdyyKEAIngzFMMvCA5d3H/Xj0zX/iy5dchDKX8bveiPIVA8948h8HPJP0roIKUCCWQkoIlDqN+QH57JvNmOC1Yd4kj651dIUTiCcFakqMGXiEEPj6k69hakk5PnXhZL3LGZQEY40pIhoNBp7xpCYAs03vKqgAdYfTy0oYcV4ZTRN49c02fOC8Cbp3ZzX3xAAY95L0J3adwO7WZnzn6oWGnkCSqBDwvzCiPNRp4MCz81g3usMJXDEOkw1m6kRPBAAM2aUViqfw3Y2v4rJZ9bhohnFnYk+oCZgVY3adEmWCgWe8aJreFVABM3ILz193t6DEefZSEHo40ROD1SwZ8jj99Pn9CMaj+K+r5utdypB64j3wWnnlGOU/Bp7x4j8G+Or0roIKVNepwOMz2ErpQgj8fddJXHReJWRZ/3EfJ7qjqHTbdO9aG8imA29h8Yy5qCkx9hVk8VQcdpPxWsiIMsXAM140FWAzMI2TznACbqsJZoON+9hzIoCTgRiWnmuMmYJbOyOoKDHmOLpLp8/G1qZ3kFLZGkyUC8Y6WxLRiHRHEvAacA6ev+5phsduwnunlupdCgCgozOKSoPOwXPt+bXojiTwamOn3qUQFQUGnvGgqYABm9CpcHQFY/DajdWCKITAi7ta8b5zKg3T8nQyEMMkg3YZ9S78GoyldK5kaFxWggqFMc5KhabnKOAz7pwalP9ioW5YXPoPCj7TwbYQTnRGsfQ8/a/OAoBoQoU/ksQkg87Bs2F7E1wWExbNqdS7FKKiwMAzHoQAZEXvKqiAJcJdsLmM0W3U69k9rXBYFLzfIJdYn+iJAgCqvcYLPKomsOGf/8RVc8+BzcxzBVEumPQugIgyF4wkMN1trA/yF95owXtmV8BqMsYHeE8kAQXAbeteh9NmgsdmgttqhsNhgtNtgddlgdduhtduhs9ugefU9167GT5H+l+HRRmXK7xePtiBtlAUn/iXqVnfNxENjIEn29QkW3do3PVEkyg10KDlY50RHD4Zwmcvn6l3KX3m1/qw9uYL0BaMwR9Nwh9NIhBJwB9KIuiPo7U1jEA0hVAsiXBCHXAfJkWC22aCy2qC22aG02mG02WG12mB13FmYDLD6zj9vcduHrLl5g/bDmJaqQ/n1Rh/fhsuK0GFgoEn23qOASX8q43GTzylIpJIGWoyvb/ubYHFJOPD9RV6l9LHpMi4bO6EEW2bUjUEYqm+YNQTSaQDUt/PSfgjCQRDSXR2RHHseBDBWArBWBKx5MCXlVtMEjw2czow2UxwOK1wuc2wWRQ8t/9tfO2yDw3YeqRpGlKqiqSaQiqVQjKVRDKlwmxSUO4zVjcmUT5h4BkPModG0fjpDicBGGuW5b/tasEFM8rgsOTnKcWkyCh1WkZ1TBMprS8o+aOJM0JT8vTtoQRCoSROtoQQiqXw/soyzK9I4K1DBwfcp2IywaQoMJtMMCkmmE0Kjre2MvAQjUF+np2IilhnOA7AOIGn1R/DO00BfPJfG/QuRRcWk4wKtxUV7vFdub6ju3tc909U6NgUkU2pOGdXpnFntBaejXtbYVIkXDp7ZN1HlD+iqSispvENckS5wsCTTf7jgLdW7yqowBmthWfTrhY0TC013ESINHb+uB8+q0/vMoiygoEn2zjDMo2z7nACVkWCw6L/1YCdoTj2HenGZecbY7JByq6kmoRFMUawJhorBh6iPNMVTsBrNxtiBfBN+05CAkZ8NRTlFy4rQYWEgSdbEhHAZMxVmamwdIaNs3Doc/9swdzJPpS5OM5jvDF6EI0NA0+2BFsAz0S9q8gdTU1Pskg5FwyF4HLovyCmP5rE7sYuLJrP7iwiMj5elp5NBuhiGDfhDiDmP/2zrACSDGip9NphA5Gk/vdJEmDzATYvZ6Meg0SwAy6f/utVvfB2G1KqwOXzqvQuhcYJZ1mmQsLAQ0PraQJSMcBRBpRNH9u+NA2I+wF/U7qFaCQkCXCUAzbP2J67gMQiIVTXTtO7DGzc1YxZkzyGXJyzEDF6EI0NA082xAJAoc1VEWgG4iHAWwNYstR9IsuAvST9NVKaBkQ6gc7GwbexegCXcZY0GG9GWEcrkkjhjQOduPkK46ydRdnHQctUSBh4ssFkA8LtQ38o9xqomwcSYHGlWzEGC07dRwChpbuRrJ70lzIOv77eoOOpNsaYJFlOh5mhAk3LP4F44PTPJivgri7IbjNNEwhEUyh16Rt4XtzfjnhKwxXszipo7NKiQsLAkw0my9i6ezQNSATToUlNDPIctnQAUVPpD/fAifT4mdGQJMDsSIenROh0ABMCcE8wRtDJRPV5/X9OxgbuNnNVAlZ37uoaB4FYEqoQurfwvH6oE7Uldkwuc+paRzHRo62FLTxUSBh4jECW0wN5bd7ht1VMgKM0/TVamgakoukWI0d54S12arYBJVPOvj14Egi1pYOdYgJ8k/NuoHlnOB2I9Z5lub0zivJSTsOQU4NdHEBEI8LAU4xkGbAU4V/m7gkATk2QpyaBrkN5F366wwkISLoHHiFJEOzuKGhxNc5ZlqmgMPBQcVLMp7shU4nT469snnTXl0EZpYWnptKBl3a26lpD0clxIO+KdqHMXpbT5yQaTwXWl0E0CiYLUD4j/aVYgI6D6a9YYPjH5lh3OAEZAj6dx/CcX1eCk4EYGttDutZB4yepcR0tKiwMPERnsvtOh59kBOh4J/1lkFmlO8MJeGwmKLK+3UmXzqmEx27CQy8f1rWOYsIORKKxYeAhGoy7CiifCZTNAPzH08Gn67Cug0e7DbKOltWk4PoPT8VfXzuOo51hvcuhccArtKjQMPAQDUeSgNKp6fDjmZQe79NxMH3VV475Q2G4HMaY2fjfL54Gl9OC+555W+9SioLIcdDmHDxUaBh4iDJx5ngfs+2M8T7+4R+bBclAG2SDDKq2WxTceuUsvPrmSbxxrFvvcoiIhsTAQzRaNu8Z431ip8LPO+mrvsZJNBqGx22cyRM/sqAGNROcuO/JfTlvgSAiygQDD1E2uCekg0/ZDCDYnA4/4zDexx9NwWs3Z3WfY6HIEr68bC7ePurHc/ty38VHRDRSDDxE2SRJ6Vmey2ekF17tG++TnTlrArGkoQIPAHxoVgXOnVmKnz71FlSNrTxEZEwMPETjRTGfMd7HMebxPoFYEuF4ChN9xlvS4UtLZ6O5M4q/vcVWnnGTBzOBExlZRoHngQceQENDAzweDzweDxYuXIhnn3227/7GxkasWLECFRUV8Hg8uP7663Hy5MhPgGvXroUkSbjjjjsGvF8IgaVLl0KSJDzxxBOZlE6kL5vndPhJxU+3/GQw3ud4VxQAUFPiGK8qR+28Wh/m1Hrx0JZDHMszXnJ4XPk7pEKUUeCpqanB2rVrsWPHDmzfvh2XXnopli9fjr179yIcDmPJkiWQJAmbN2/GK6+8gkQigWuuuQaapg27723btmHdunVoaGgYdJsf//jHkPhXDuU7V2V6WYuy6UCwJR18OhuBRGTIhx3rCkOCwJQy4wUeAPjUJdOw/0gP/rK7Re9SaIyiqSjsJmNMf0CULRmtpXXNNdf0+/m73/0uHnjgAbz22ms4ceIEjhw5gjfeeAMejwcA8NBDD6GkpASbN2/G4sWLB91vKBTCv/3bv+FXv/oV7r777gG32bVrF374wx9i+/btqK6uzqRsImOSJKBk8umfA83pANTLMyl96fspvX90P7O7BdMqXCh1WlDqtKDEYYHFlLveaX8kiV3He/B2SwBH2wLoOHoCJztDaA/FUaFKePrlRlzdMDFn9VD2dce7UekwxvQHRNky6sVDVVXFhg0bEA6HsXDhQjQ2NkKSJFit1r5tbDYbZFnGyy+/PGTgue2223DVVVdh8eLFAwaeSCSCf/3Xf8XPf/5zVFVVjai+eDyOeDze93MgYLx1kYj68ZwREjQNCJwA1NNdXnVSALU97bj/4RfPeqjTqqDEYYbbYYW91AdnqQ+lLhvKXRaUuSwod1lR5rSi3G1BqcMCkzJ8QBJCoD0Yx4GTIRw4GcTbR9px9MARHOuKQoKA3aygusyFksmTcPG5MzC51Im6MgcqjzVBS6mQTUpWDgvlnqqpMMvGGhxPNFYZB57du3dj4cKFiMVicLlcePzxxzF37lxUVFTA6XRi9erVuOeeeyCEwJ133glVVdHSMngT9/r167Fz505s27Zt0G1WrVqFiy66CMuXLx9xnffeey++/e1vZ/TaiAxDlgFfbb+bzikDfr26BMnqGnSFEugMx9EVTqAznEBXOIHucAJdwRginV3oOHAI70QS8EeSiCVVCEiQzlgqwGszweu0wmc3QzYpUC02SLIMRZaQDIUQjibRHowhFEuvIWYyyaib4EP9gjn4eF0Jzq8rwZQyx4BdzNqUErS+ug8TL543vseIiCgDGQee+vp67Nq1C36/H48++ihuuukmvPjii5g7dy42bNiAW2+9FT/96U8hyzI++clPYsGCBZDlgf+abGpqwsqVK7Fp0ybYbANfefLkk09i8+bNeOONNzKqc82aNfjSl77U93MgEEBtbe0QjyAyPkmS4LKa4LKaUDfCsTyRRAqdoQQ6QnF0ngpKHaEEOkMJdEcSUFMpWGMxqJoGTQg4ayah2mnFRR4bZlS6MGuCG3WljhEvWCrLMiovmIHW195C1YVzxvJyiYiyJuPAY7FYMGPGDADABRdcgG3btuEnP/kJ1q1bhyVLlqCxsREdHR0wmUzw+XyoqqrCtGnTBtzXjh070NbWhgULFvTdpqoqXnrpJfzsZz9DPB7H5s2b0djYCJ/P1++x1113HT7wgQ9gy5YtA+7barX2614jyndC09ItPxlyWExwlJpQW5q7wc4muw0lsyahfddBVMyfkbPnJSIazKjH8PTSNK3fWBkAKC8vBwBs3rwZbW1tWLZs2YCPXbRoEXbv3t3vtptvvhmzZ8/G6tWroSgK7rzzTnzmM5/pt825556LH/3oR2cNoiYqZKrfD+XUBQH5wFrqgT0aR/fbx1Ayu07vcoioyGUUeNasWYOlS5eirq4OwWAQjzzyCLZs2YKNGzcCAB588EHMmTMHFRUV2Lp1K1auXIlVq1ahvr6+bx+LFi3CihUrcPvtt8PtdmPevP79/E6nE2VlZX23V1VVDThQua6uDlOnTs34BRPlKy0YhLmmRu8yMuKaVAF/4wkEjrTCM2VkFxzQwFRNw77Gg7BbbaiuqICNLdhEGcko8LS1teHGG29ES0sLvF4vGhoasHHjRlx22WUAgP3792PNmjXo6urClClTcNddd2HVqlX99tHb5UVEGRIC0ii6tPTmnT4JnXuOINLaBUdVqd7l5K15M2YBAIKREI63tSKZTA79ACFBURRU+Erh87ghjXAMFlGhkkSRTKkZCATg9Xrh9/v75gkiyieJo0dhmTx5+A0Nqm37AXhnToLV69S7lKKRSCVwsrsdoVAMEKdnUE5fXCcBZ1y5J8kyKkpKUOL24njoOOo87IYkY8jW5/eYx/AQEY1E5XtmofmVvZjwL/VQLDz15ILFZEFtxSSgYvhtVVXFye5OHDh6GLaDLUjMOhWGhEgnpN6/jc/4Xna5oPh8kEz8fZLx8V1KRDlTtXAOWl7ei+qLzxl0ugrSh6IomFheiYnllWjvEcO2JgohoIUjSLa0AKp6dig6vWH6X5MZptISyA5jLo1ChY+Bh4hyRpZlVL2vHidfewvVF52jdzk0gIQ/BJNz+HW0JEmC4nJCcY2si1Ikk0h1dSPV1nbmTt610RldbHYHTKUlkMyc8Zmyg4GHiHJKsVpQOrcObTsOoPKCWXqXQ+8SONaOklnZvxpQMpthnjDy9bm0SASptjaIVCp9w5nDTQeY4Vv2eKB4vXk5sJ9yg4GHiHLO6nPDNTGOzr1HUHbOFL3LoTOIZAqKVf9WFdnhGHH3lxACWiCA5PHjp4PRENfjSA4HTKWlHHtUZPjbJiJdOKrLkYol4G9shnc6V1en0ZMkCYrXC8XrHdH2WjiMZGsr0Nt6dHpH/X8WArLbnR6YrXAx3HzHwENEuvFMnYjufUcQOtEB16RyvcspepqmQaDwZyqRnU5YnMOPPUoPzA4j2dx8emB2+o70v+8aqC17POlwNECXG+mPgYeIdFUydwradh6AyW6FrdStdzlFreetY/DNzK/ZvMdTemC2C4rLNey2Qghofj+Sx44NGoj6vpdlKCUlkF0uhqMcYuAhygNaNArJZtO7jHFTuWAWWl7di4rzZ8Jkt+hdTtFKRuKwuHnZ+GhIkgTF54PyroWuByJUFWpPz9DhqJeiwFRaysv5s4CBhygPqN3dUMoLu8unauFcNP99N6ovnsc5enQQaemArcKndxlFQVIUmMrKgLKyYbcd8HL+vh2dHZJ4tdrgGHiI8oBIJCBbCrvlQ5IkVF84B62v7sPEi+cN/wDKquDxTkz4l/rhN6ScyuRy/n5Xq2nawBNAnhGSJKsVSllZwZ9bejHwEJFhyBYzKs6bitZt+1HFD9+cSUVjUKz8OMh3GV+tFotB7ehAKpkcumtNCEhmczoc5XHXOt/hRGQoZrcT3smV6Nx9GGXnTtW7nKLQuecoKi6YqXcZlGOyzQZ54simhBCJBFJdXUjF40NuZ+QFjhl4iMhw7JUlSEXj6H7nBEpmTtK7nKLAcVM0FMligbmqashtEkeP5qia0eE7nIgMyT25CpKaQrBpgMGalDWde46gdK5x/yonyhYGHiIyLN/syYh3+hHt8OtdSsFKReMwO/N3XAbRSDHwEJGhlc+fCf/BE0iGY3qXUnDCLZ1wVI5sgCtRvmPgIcoHRT4b64T3zUHbjgNQVU3vUgpK+EQn3JOHHpdBVCgYeIjywRArPxcDSZJQfdFctG7dp3cphaW4czQVGQYeIsoLssmEyvOnofUfb+tdSkHo2nsEvvpavcsgyhkGHiKDE0IUfZdWL7PTAd/UCWh/85DepeS9VCwBi8uudxlEOcPAQ2RwWjAI2ePRuwzDsFWUwF7iRPf+Jr1LyVuaxrFQlF1C0wCDz+Vk7OqICKrfD4WBpx9X7QTIkkDgGOfoGY3g4VZ4pnCwMmVPPpynGHiIjE7TICmK3lUYjndWHZL+ICJtnKMnU4GjbbCVGfvDifJLPrREM/AQUd4qO3c6QkeaEQ9F9S4lr9hKXHqXQIVGCEgGH2vIwENEea3yvXPQufMdaClV71KIyMAYeIgo71VddA5aX+fl6iORisUhm7luNGWZwVt3AAYeIioAsklBycyJ6Np3RO9SDC94tA3uugq9y6BCkweTozLwEFFBsFeWQAIQaevWuxRDS0XisHicepdBhYYtPEREuVMydwr8jc16l0FEBsTAQ0QFpbxhGtp3HdS7DCIyGAYeIiooZqcdJqsZsc6A3qUQFQUtkdC7hBFh4CGiglMyZzJ62LVFNO6SLS1ItbTAUlendynDYuAhMjChaXkxGNCIPDXl8B9u0bsMw5Fkvp9o7LRIBPGDByG7PbBMnqx3OSPCwENkYFowCNnt1ruMvOSYWI5YB5edeDehGf/yYTK2RFMTUl3dsM6YAcWVP1f8MfAQGZgaCBh+QT4j882chO63j+ldBlFBUINBxA8ehKmiApaaSXqXkzFOt0lkZFw4dEysPjd63smfsTzNr+yF2WEFcLrrqbdFRpKljFtnZLMJFp8TVp8LJocVQtUgmfh+oswIIZA4cgSy3Q7rjBl6lzNqDDxEVNB806vRvb8JJfW1epcyLIvLhvLzpmdtf6lIDAl/GOETHUiFYxBCoPSc/BhvQcaQ6u6G2tEBy5QpkMxmvcsZEwYeIipo1lIPehrzY/BytsfXmBw2mBw2OKrLsrpfKnxCCCQOHYLsdsM6c6be5WQFAw8RFTxHhQfhli44q0v1LmVQcX8IZpdN7zKIoAaDSLa0wDpjBiS5cIb6Fs4rISIahHtKNULH2/UuY0jBo21wT6nSuwwqcsnmZmjBIGyzZhVU2AHYwkNERUTTNMgGPYkLVYNi5imZ9BM/dBimslIoXq/epYwLY/6XT0SUZaXzJqNrz1G9yyAyHCEEYvv3wzyxumDDDsAWHiIqEma7DVoyqXcZRIYiNA3x/fthra8vuC6sd2PgIaKiIZsUtO98B4rVDOfEMpi9TsN2cRGNN6GqiB84AOvs2ZCKYAkbBh4iKhq9c9ykIjGEWzoROHJy4A3FqcvDz/gQsJY44awuh2Idp7lIBJd8oNwRQqRbdubMKYqwAzDwEBmW0DSArQ/jwuSwwTt95FPja5qGeFcIPe+cgJZMjegxkkmBs7oU1lI3W5HIcPq6sYok7AAMPESGpQUCULhwqCHIsgx7uQf28pGva6bGk4i0diLUNPzl8ELTYHZyDh7KjfjBg7BOm1Z0y9Yw8BAZlBoIwFxTo3cZNEqK1Qz35Cq4uZIDGUji+AmYJkyAZLHoXUrOsZ2VyKiEKPirJogod1KdnZAs5qJtOebZlIiIqMBpsRi0YBDmykq9S9ENAw8REVGBSxw7BsuUKXqXoSsGHiIiogKWOHYMltpavcvQHQMPERFRgdLCYUgmE2S7Xe9SdMfAQ0REVKCSLS0wT5yodxmGwMBDRERUgJItLTBVVeldhmEw8BARERUYIQS0cBiKy6V3KYbBwENERFRgEocPwzJ1qt5lGAoDDxERUQHRolFIFmvRLR0xHAYeIgMSqsqFQ4loVJLNzbDUjHxx3GKR0Rn1gQceQENDAzweDzweDxYuXIhnn3227/7GxkasWLECFRUV8Hg8uP7663Hy5MkR73/t2rWQJAl33HFH321dXV34j//4D9TX18Nut6Ourg5f/OIX4ff7MymdKK+ogQAUr1fvMogoz6S6u6H4fHqXYUgZBZ6amhqsXbsWO3bswPbt23HppZdi+fLl2Lt3L8LhMJYsWQJJkrB582a88sorSCQSuOaaa6Bp2rD73rZtG9atW4eGhoZ+tzc3N6O5uRk/+MEPsGfPHvz2t7/FX//6V9xyyy2ZvVKiPKIFg5CLdL0bIho9tasLprIyvcswJEkIIcayg9LSUtx3332ora3F0qVL0d3dDY/HAwDw+/0oKSnBc889h8WLFw+6j1AohAULFuD+++/H3Xffjfnz5+PHP/7xoNtv2LABn/rUpxAOh2EyjWzB90AgAK/XC7/f31cfkVEljh6FZTKX2SaikUs2N0PxeiE7nXqXklXZ+vwe9SABVVWxfv16hMNhLFy4EPF4HJIkwWq19m1js9kgyzJefvnlIfd122234aqrrhoyFJ2p90UPFXbi8TgCgUC/LyIiokIkhIAWjRZc2MmmjAPP7t274XK5YLVa8YUvfAGPP/445s6diwsvvBBOpxOrV69GJBJBOBzGV77yFaiqipaWlkH3t379euzcuRP33nvviJ6/o6MD3/nOd/C5z31uyO3uvfdeeL3evq9ariNCREQFKnH4SNEvDjqcjANPfX09du3ahddffx233norbrrpJuzbtw8VFRXYsGEDnnrqKbhcLni9XvT09GDBggWQB7napKmpCStXrsTDDz8Mm8027HMHAgFcddVVmDt3Lr71rW8Nue2aNWvg9/v7vpqamjJ9qUT6kSS9KyCiPKHFYpAsZl6GPowxj+FZvHgxpk+fjnXr1vXd1tHRAZPJBJ/Ph6qqKnz5y1/GV7/61bMe+8QTT2DFihVQzvglqaoKSZIgyzLi8XjffcFgEJdffjkcDgeefvrpEQWkM3EMD+UTjuEhopGKNzbCOn263mWMm2x9fo9sxO8QNE1DPB7vd1t5eTkAYPPmzWhra8OyZcsGfOyiRYuwe/fufrfdfPPNmD17NlavXt0XdgKBAC6//HJYrVY8+eSTGYcdIiKiQpTq7uYUFiOUUeBZs2YNli5dirq6OgSDQTzyyCPYsmULNm7cCAB48MEHMWfOHFRUVGDr1q1YuXIlVq1ahfr6+r59LFq0CCtWrMDtt98Ot9uNefPm9XsOp9OJsrKyvtsDgQCWLFmCSCSC3//+9/0GIFdUVPRrHSIiIiomaldXQbfuZFNGgaetrQ033ngjWlpa4PV60dDQgI0bN+Kyyy4DAOzfvx9r1qxBV1cXpkyZgrvuugurVq3qt4/GxkZ0dHSM+Dl37tyJ119/HQAwY8aMfvcdPnwYUzhIiwoRx/AQ0TCSzc0wczX0ERvzGJ58wTE8lE84hoeIhiKEQOLQoaJo3dF9Hh4iIiLSBy9DzxwDD5HBiFQK4Ng0IhoEL0MfHQYeIoPhwqFENJTkiROw1NToXUbeYeAhMhgtFILsculdBhEZEC9DHz0GHiKjEQISr9IiogGoXV0wnZrrjjLDwENERJQHki0tME3gZeijxcBDRERkcEIIaJEIFBdXQx8tBh4io2F3FhG9S+IIL0MfKwYeIiIiAxOJBCRF4WXoY8TAQ2Q0xTH5ORGNUKKpCebaWr3LyHsMPERERAalhsKQ7XZeuZkFDDxERsMTGxGdkjrZCvPEiXqXURAYeIiIiAyIkwxmFwMPERGRAand3ZxkMIsYeIgMRKRSgMwrMYiKnRoIQHG79S6joDDwEBmI6vdD8bEJm6jYpTo6Yaqo0LuMgsLAQ2QgWjgM2cmZVImKmUilIJnY0pttDDxEBsPLT4mKW/L4cc67Mw4YeIiMhJMOEhU9IQT/8BkHDDxEREQGofb0QPH59C6jIDHwEBkJ/6ojKmpqTw9MJSV6l1GQGHiIiIio4DHwEBERGYAaCkF2ufQuo2Ax8BAZCQctExUttaODMyuPIwYeIiIiKngMPERERFTwGHiIiIh0pgaDHL8zzhh4iAxCJJOAYtK7DCLSgdrZyfE744yBh8gg0vNv+PQug4ioIDHwEBmEFolAcjj0LoOIqCAx8BAZCNfPISIaHww8REREVPAYeIiIiHQkUilAUfQuo+Ax8BAZBWdZJipKqfZ2mCoq9C6j4DHwEBkFx+8QFSWRSEC22fQuo+Ax8BAZBVt4iIjGDWc5IyIiQxKJBBLHT0BS0n+bSyYTlNJSyHa7zpVRPmLgITIKdmkR9dEiESSbm2GdMaPvNpFIINXdg1Rb2+n/XoZoGZVdLig+HySjDwhm625OMPAQEZGhqH4/1O7ufmEHACSLBeYJlSPahxACWjiCZEsLoKrDP0BRoPhKIDsdnA+rQDHwEBmA0DS28BABSHV2QsRisEyZMqb9SJIExeWE4nKOaHuRSkHt6UGys2Nk+7fZYSotgWQ2j6XMUzvjf/u5wMBDZACq3w/F69W7DCJdJVtbAUmCedKknD+3ZDKlF+8c4QKeWiSC5Mk2QE0NvEFvN5Uk9X0vu90wlZUNvi2NKwYeIgPQAgGY6+r0LoNIN4njxyE7HDCVlupdyojIDgcsGa59pwYCSBw9etb4I66hlxsMPEQGwXEDVKzihw/DVFYGxePRu5RxpXg8Bf8ajYyBh4iIdCGEQOLgQZhranipOY07Bh4iIso5oWmIHzgA6/Tp2Rn4SzQMBh4iIsopLZFA4vBhWOvr2ZVLOcPAQ0REOaOFw0i2tMBWX693KVRkGHiIiCgnVL8fak/PWRMKEuUCAw8REY27VEcHRDwOy+TJepdCRYqBh4iIxlV6QkFZlwkFiXrJehdARESFK9HUlNEaWETjhS08RDoTqRRg9NWciUYhfugwTBXlUNxuvUshYuAh0pva0wPFV6J3GURZI4RA/MA7sNTVckJBMgx2aRHpTAuHITu5lg4VBqGqiL/9NqzTpzHskKGwhYfIADj5GhWCvgkFZ8/me5oMh4GHiIjGTA2FkTrZygkFybAYeIiIaEzUnh6ofj+s06frXQrRoBh4iIho1FIdHRCJBCcUJMPjoGUiIhqVZEsLhKbBPHGi3qUQDYstPERElLFEUxNklwumEk6pQPkhoxaeBx54AA0NDfB4PPB4PFi4cCGeffbZvvsbGxuxYsUKVFRUwOPx4Prrr8fJkydHvP+1a9dCkiTccccd/W6PxWK47bbbUFZWBpfLheuuuy6j/RIRUfbEDx2GUlLCsEN5JaPAU1NTg7Vr12LHjh3Yvn07Lr30Uixfvhx79+5FOBzGkiVLIEkSNm/ejFdeeQWJRALXXHMNNE0bdt/btm3DunXr0NDQcNZ9q1atwlNPPYUNGzbgxRdfRHNzMz7ykY9kUjqRcfHyXcoTQgjE9h+AeWI1FJdL73KIMiIJIcRYdlBaWor77rsPtbW1WLp0Kbq7u+HxeAAAfr8fJSUleO6557B48eJB9xEKhbBgwQLcf//9uPvuuzF//nz8+Mc/7ttHRUUFHnnkEXz0ox8FALz99tuYM2cOtm7digsvvHBEdQYCAXi9Xvj9/r76iIwgcfQoB3yS4QlVRfzAAVhnzoRk4mgIyp1sfX6PetCyqqpYv349wuEwFi5ciHg8DkmSYLVa+7ax2WyQZRkvv/zykPu67bbbcNVVVw0Yinbs2IFkMtnvvtmzZ6Ourg5bt24ddJ/xeByBQKDfF5HRaIkEJLNZ7zKIhqTF44gfPJieUJBhh/JUxoFn9+7dcLlcsFqt+MIXvoDHH38cc+fOxYUXXgin04nVq1cjEokgHA7jK1/5ClRVRUtLy6D7W79+PXbu3Il77713wPtbW1thsVjg8/n63T5hwgS0trYOut97770XXq+376u2tjbTl0o07tTubiilpXqXQTQoNRRG8vhx2OrrOXsy5bWMA099fT127dqF119/Hbfeeituuukm7Nu3DxUVFdiwYQOeeuopuFwueL1e9PT0YMGCBZDlgZ+mqakJK1euxMMPPwybzTbmF3OmNWvWwO/39301NTVldf9E2SBiMchZfu8TZUuquxtqVycnFKSCkHHbpMViwYwZMwAAF1xwAbZt24af/OQnWLduHZYsWYLGxkZ0dHTAZDLB5/OhqqoK06ZNG3BfO3bsQFtbGxYsWNB3m6qqeOmll/Czn/0M8XgcVVVVSCQS6Onp6dfKc/LkSVRVVQ1ap9Vq7de9RkREI5dqb4dIJmGpq9O7FKKsGHNnrKZpiMfj/W4rLy8HAGzevBltbW1YtmzZgI9dtGgRdu/e3e+2m2++GbNnz8bq1auhKAouuOACmM1mPP/887juuusAAPv378exY8ewcOHCsZZPpC92EZABJZubAZOJEwpSQcko8KxZswZLly5FXV0dgsEgHnnkEWzZsgUbN24EADz44IOYM2cOKioqsHXrVqxcuRKrVq1C/RmLyS1atAgrVqzA7bffDrfbjXnz5vV7DqfTibKysr7bvV4vbrnlFnzpS19CaWkpPB4P/uM//gMLFy4c8RVaREYlkkm9SyDqJ3HsGGS3m3PsUMHJKPC0tbXhxhtvREtLC7xeLxoaGrBx40ZcdtllANItL2vWrEFXVxemTJmCu+66C6tWreq3j94ur0z86Ec/gizLuO666xCPx3H55Zfj/vvvz2gfREbEK16MQUskkDx2DNZT3fXFKn7oEEyVlZxjhwrSmOfhyRech4eMiHPw5F6yuRlaLA5Jkfu6FCVFgamiAokiDT1CCMQPvAPLlMmQOfaRDCZbn9/885JIR2ogCDUUhux08JLfHNFicVinTR3wPlN5OVLt7TBVVOS4Kv0IVUX8nXfSEwoqit7lEI0bBh4iHdnmzIba04Nk58i7eWWXC4rXy+6wUUgcPwHzpMEH4io+HxJHj+awIn1psVi6VYtz7FAR4BmTSEeSyQRTeTlw6srG4QghoIXDSLaeBDQVGKhH+l0fXJLZDKWkhPP9ABDJBLtsTlFDIaTa2mCbNUvvUohygoGHKI9IkgTF5cpoUKkWj0Pt7kYqHu8fht4dlnrvk2UoXi9kl6vg/uof0espgmGNqe5uaMEgrIPMkUZUiBh4iAqcbLVCHmKSzncTqRRUvx/JY8dO3yhJQwYB2e1Od7MZeAxIqqMDykha0hQFQlUN/VrGItnWBqgqJxSkosPAQ0T9SCYTTGVlQFnZiLYXQkALBtOT1WnaSB4AyW6HqaQEksUyxmpHRqgqUh0dsM2ePey25qoqJFtaYamZlIPKcivZ3AzJbIapulrvUohyjoGHiMZEkiQoHg+UDC4X1aJRpDo6+k+82NuC9O7WJEmCZLPDVFqS8cryQggkjx6FEALWMyZAHYpkNgNqKqPnyQeJo0chezycUJCKFgMPEeWcbLdDtttHvL0WiSB5sg1IJQceh9R7mxCALEN2u6H29ABCwFJXl/kVbQU2jid+8CBMVVWcUJCKGgMPERme7HDA4nCMaFuhqtBCIVgmT4Yky+NcmbEJIRDfvx+WqVN5dRoVPQYeIiookqJA8XrHtA/Z7UbiyJEBdi5B9njSA7QNHqZEKpWeUHDWrIIdgE2UCQYeIqJ3GWzQthACmt+P5PHj/bu9BrnEX3a7ofh8OQ9HWiyGxNFjsM6eXXBTCxCNFgMPEdEISZIExeeD4vMNu23f1WvvDkcDb5yeQbukZMytMWowiFR7O2z1nFCQ6EwMPERE4yCTq9f6ZtAe6NL+Mwdkn/pXcjhgKi09azA2JxQkGhwDDxGRzjKdQTu9vEgroKpn7gSSxcIJBYkGwcBDRJRnZKcTFqdT7zKI8oqxLzMgIiIiygIGHiIiIip4DDxERERU8Bh4iIiIqOAx8BAREVHBY+AhIiKigsfAQ0RERAWPgYeIiIgKHgMPERERFTwGHiIiIip4DDxERERU8Bh4iIiIqOAx8BAREVHBY+AhIiKigmfSu4BcEUIAAAKBgM6VEBER0Uj1fm73fo6PVtEEnmAwCACora3VuRIiIiLKVDAYhNfrHfXjJTHWyJQnNE1Dc3Mz3G43JEka1T4CgQBqa2vR1NQEj8eT5QrzA48BjwHAY9CLx4HHAOAxAMb3GAghEAwGMXHiRMjy6EfiFE0LjyzLqKmpycq+PB5P0b6pe/EY8BgAPAa9eBx4DAAeA2D8jsFYWnZ6cdAyERERFTwGHiIiIip4DDwZsFqt+OY3vwmr1ap3KbrhMeAxAHgMevE48BgAPAZAfhyDohm0TERERMWLLTxERERU8Bh4iIiIqOAx8BAREVHBY+AhIiKiglc0gefAgQNYvnw5ysvL4fF4cPHFF+OFF14YcNvOzk7U1NRAkiT09PSMaP/xeBzz58+HJEnYtWtXv/vefPNNfOADH4DNZkNtbS2+//3vn/X4DRs2YPbs2bDZbDj33HPxzDPPZPoShzVex2DZsmWoq6uDzWZDdXU1brjhBjQ3N/fd/61vfQuSJJ315XQ6+7b57W9/e9b9NpstK6/73fQ6DkeOHBnwOLz22mv99lPI74UtW7Zg+fLlqK6uhtPpxPz58/Hwww/320eu3gt6HQOgsM8JR44cwS233IKpU6fCbrdj+vTp+OY3v4lEItG3TTGcE0ZyHAr9nDCSY5DLc0LRBJ6rr74aqVQKmzdvxo4dO3Deeefh6quvRmtr61nb3nLLLWhoaMho///5n/+JiRMnnnV7IBDAkiVLMHnyZOzYsQP33XcfvvWtb+GXv/xl3zavvvoqPvnJT+KWW27BG2+8gWuvvRbXXnst9uzZk/kLHcJ4HYNLLrkEf/rTn7B//3489thjaGxsxEc/+tG++7/yla+gpaWl39fcuXPxsY99rN9+PB5Pv22OHj06thc8CL2OQ6+//e1v/V7nBRdc0Hdfob8XXn31VTQ0NOCxxx7Dm2++iZtvvhk33ngjnn766X77ycV7Qa9jUOjnhLfffhuapmHdunXYu3cvfvSjH+EXv/gFvva1r/VtUwznhJEch16Fek4YyTHI6TlBFIH29nYBQLz00kt9twUCAQFAbNq0qd+2999/v/jQhz4knn/+eQFAdHd3D7v/Z555RsyePVvs3btXABBvvPFGv/2VlJSIeDzed9vq1atFfX1938/XX3+9uOqqq/rt833ve5/4/Oc/n+ErHdx4H4Mz/fnPfxaSJIlEIjHg/bt27TqrlgcffFB4vd6Mnmc09DwOhw8fPuv98W7F9l4QQogrr7xS3HzzzX0/5+K9oOcxKMZzwve//30xderUQe8vlnPCu49DMZ4ThnsvCDF+54SiaOEpKytDfX09fve73yEcDiOVSmHdunWorKzsl6T37duH//7v/8bvfve7ES9QdvLkSXz2s5/F//7v/8LhcJx1/9atW/HBD34QFoul77bLL78c+/fvR3d3d982ixcv7ve4yy+/HFu3bh3Nyx3QeB6DM3V1deHhhx/GRRddBLPZPOA2v/71rzFr1ix84AMf6Hd7KBTC5MmTUVtbi+XLl2Pv3r0ZP/9wjHAcli1bhsrKSlx88cV48skn+91XbO8FAPD7/SgtLe1323i/F/Q8BsV2TgAG/h2fqRjOCcDgx6FYzgnA8O+FwbbJynthzJEpTzQ1NYkLLrhASJIkFEUR1dXVYufOnX33x2Ix0dDQIP73f/9XCCHECy+8MGx61TRNXHHFFeI73/mOEGLgtH7ZZZeJz33uc/0e19sStG/fPiGEEGazWTzyyCP9tvn5z38uKisrx/KSzzIex6DXf/7nfwqHwyEAiAsvvFB0dHQMuF00GhUlJSXie9/7Xr/bX331VfHQQw+JN954Q2zZskVcffXVwuPxiKamptG/4EHodRza29vFD3/4Q/Haa6+Jf/zjH2L16tVCkiTx5z//uW+bYnovCCHEH//4R2GxWMSePXv6bsvVe0GvY1As54Re77zzjvB4POKXv/zlgPcX+jmh10DHoVjOCb2Gey8IMb7nhLwOPKtXrxYAhvx66623hKZpYtmyZWLp0qXi5ZdfFjt27BC33nqrmDRpkmhubhZCCLFq1Srx8Y9/vG/fI/ll/uQnPxHvf//7RSqVEkLoE3j0Pga92tvbxf79+8Vzzz0n3v/+94srr7xSaJp21naPPPKIMJlMorW1dcj9JRIJMX36dPH1r3992OfOx+PQ64YbbhAXX3xx38/F9F7YvHmzcDgc4qGHHhpyf5m8F/LhGBTLOUEIIY4fPy6mT58ubrnllkG3KfRzghAjOw69CvGcMNJjMB7nhDPldeBpa2sTb7311pBf8Xhc/O1vfxOyLAu/39/v8TNmzBD33nuvEEKI8847T8iyLBRFEYqiCFmWBQChKIr4xje+MeDzL1++vN9jFEXpe8yNN94ohEi/eZcvX97vcZs3bxYARFdXlxBCiNraWvGjH/2o3zbf+MY3RENDg+GPwUCampoEAPHqq6+edd+ll14qrr322hHt56Mf/aj4xCc+MaJt8+049PrZz34mqqqq+n4ulvfCli1bhNPpFOvWrRvRfkb6XsiHY1As54QTJ06ImTNnihtuuEGoqjrodoV+ThjpcehViOeEkRyD8TonnMk0aF9XHqioqEBFRcWw20UiEQA4q89RlmVomgYAeOyxxxCNRvvu27ZtG/793/8df//73zF9+vQB9/vTn/4Ud999d9/Pzc3NuPzyy/HHP/4R73vf+wAACxcuxF133YVkMtnXh79p0ybU19ejpKSkb5vnn38ed9xxR9++Nm3ahIULFw772vQ+BgPp3V88Hu93++HDh/HCCy+c1Uc9EFVVsXv3blx55ZUjes58Og5n2rVrF6qrq/t+Lob3wpYtW3D11Vfje9/7Hj73uc8Nu49M3gv5cAyK4Zxw4sQJXHLJJbjgggvw4IMPDjreo9DPCSM9DmcqtHPCSI7BeJ4T+skoHuWp9vZ2UVZWJj7ykY+IXbt2if3794uvfOUrwmw2i127dg34mIGa615//XVRX18vjh8/PuBjBurS6unpERMmTBA33HCD2LNnj1i/fr1wOBz9Uuwrr7wiTCaT+MEPfiDeeust8c1vflOYzWaxe/furLx+IcbvGLz22mvif/7nf8Qbb7whjhw5Ip5//nlx0UUXienTp4tYLNZvf1//+tfFxIkT+7oAz/Ttb39bbNy4UTQ2NoodO3aIT3ziE8Jms4m9e/dm7RgIoe9x+O1vfyseeeSRvr+svvvd7wpZlsVvfvObvv0W+nuht8l6zZo1oqWlpe+rs7Ozb7+5eC/oeQwK/Zxw/PhxMWPGDLFo0SJx/Pjxfr/ndyvkc8JIjkOhnxNGcgxyeU4oisAjhBDbtm0TS5YsEaWlpcLtdosLL7xQPPPMM4NuP9Avs/e2w4cPD/iYwS4x/Oc//ykuvvhiYbVaxaRJk8TatWvPeuyf/vQnMWvWLGGxWMQ555wj/vKXv4zmZQ5pPI7Bm2++KS655BJRWloqrFarmDJlivjCF75wVihUVVXU1NSIr33tawM+1x133CHq6uqExWIREyZMEFdeeWW/AXPZpNdx+O1vfyvmzJkjHA6H8Hg84r3vfa/YsGHDWc9XyO+Fm266acBxBB/60If6tsnVe0HP/x4K+Zzw4IMPDjpe5EyFfk4YyXEo9HPCSI5BLs8JkhBCZNYmRERERJRfimIeHiIiIipuDDxERERU8Bh4iIiIqOAx8BAREVHBY+AhIiKigsfAQ0RERAWPgYeIiIgKHgMPERERFTwGHiIiIip4DDxERERU8Bh4iIiIqOAx8BAREVHB+/8Ba+Mm2VzQXeIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"spot check of new unit nbr lists ...\")\n",
    "for u in [5, nUnits -1]:\n",
    "    plotPoly(unitGeom[u])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "78674c73-ccf7-48a3-94f9-73299c33eece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0342795696883447\n"
     ]
    }
   ],
   "source": [
    "print(np.max([unitPop[u]/aDP for u in range(nUnits)] ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "6684a743-8342-46ca-8c92-054ea34dc6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "identify the border units.  Numbered units have zero nonborder neighbors; will be surrounded next block\n",
      "'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"identify the border units.  Numbered units have zero nonborder neighbors; will be surrounded next block\")\n",
    "print(\"'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for b in borderBGs:\n",
    "    u = tractUnitNo[b]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits.append(u)\n",
    "    if unitPop[u] > 0.5 * aDP:\n",
    "        plotCenter(\"pop\"+str(r3(unitPop[u]/aDP)),unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "d8ac804b-fedc-457c-bf73-2eb0169b4077",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will collapse unit 2067 into unit 2066\n",
      "we will collapse unit 2070 into unit 684\n",
      "we will collapse unit 2081 into unit 3151\n",
      "we will collapse unit 1573 into unit 2571\n",
      "we will collapse unit 2604 into unit 1284\n",
      "we will collapse unit 65 into unit 97\n",
      "we will collapse unit 66 into unit 97\n",
      "we will collapse unit 2126 into unit 2384\n",
      "we will collapse unit 2676 into unit 3066\n",
      "we will collapse unit 2221 into unit 481\n",
      "we will collapse unit 1761 into unit 2134\n",
      "we will collapse unit 2298 into unit 2339\n",
      "we will collapse unit 2299 into unit 2339\n",
      "we will collapse unit 2300 into unit 2340\n",
      "we will collapse unit 1795 into unit 943\n",
      "we will collapse unit 2366 into unit 2349\n",
      "we will collapse unit 844 into unit 2134\n",
      "we will collapse unit 1437 into unit 1468\n",
      "we will collapse unit 1463 into unit 1468\n",
      "we will collapse unit 3043 into unit 1826\n"
     ]
    }
   ],
   "source": [
    "surrounderBorderUnits = list()\n",
    "for u in surroundedBorderUnits:\n",
    "    candidates = list()\n",
    "    for uu in set(unitNbrs[u]).difference(set(surroundedBorderUnits)):\n",
    "        candidates.append(uu)\n",
    "    uuDists = [unitCP[u].distance(unitCP[uu]) for uu in candidates]\n",
    "    uuu = candidates[uuDists.index(np.min(uuDists)) ]\n",
    "    print(\"we will collapse unit\",u,\"into unit\",uuu)\n",
    "    surrounderBorderUnits.append(uuu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "45dc8100-0e51-47e1-9d17-9cb204e7fed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in surrounderBorderUnits:\n",
    "    if u in surroundedBorderUnits:\n",
    "        print(\"uhoh, we named\",u,\"as a surrounder but it was surrounded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "0eb4bffe-b45e-4a02-a050-b9191c45e897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absorbing surrounded border units ...\n"
     ]
    }
   ],
   "source": [
    "oldList = [unitTractList[u].copy() for u in range(nUnits)] #safekeeping\n",
    "print(\"absorbing surrounded border units ...\")\n",
    "for u in range(nUnits):\n",
    "    if u in surroundedBorderUnits:\n",
    "        uu = surrounderBorderUnits[surroundedBorderUnits.index(u)]\n",
    "        oldList[uu] += oldList[u]\n",
    "        oldList[u] = list()\n",
    "unitTractList, unitPop = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u not in surroundedBorderUnits:\n",
    "        unitTractList.append(oldList[u])\n",
    "        unitPop.append(np.sum([tractPop[b] for b in oldList[u] ]))\n",
    "nUnits = len(unitPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "212001f0-4b1d-4a5c-a2f1-7426cae08720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating reverse bg assignments ...\n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on bg topology\n",
      "build unit nbr list based on adjacent bg's\n"
     ]
    }
   ],
   "source": [
    "print(\"updating reverse bg assignments ...\")\n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "\n",
    "print(\"reassign unit nbrs based on bg topology\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent bg's\")  #these are list-based, not geom-based\n",
    "for bg in range(nTracts):\n",
    "    u = tractUnitNo[bg]\n",
    "    for bb in bgNbrs[bg]:\n",
    "        uu = tractUnitNo[bb]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ec34ed82-e9fd-454d-b368-3ba7367cbf64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rebuilding geom for unit 0\n",
      "rebuilding geom for unit 500\n",
      "rebuilding geom for unit 1000\n",
      "rebuilding geom for unit 1500\n",
      "rebuilding geom for unit 2000\n",
      "rebuilding geom for unit 2500\n",
      "rebuilding geom for unit 3000\n"
     ]
    }
   ],
   "source": [
    "unitGeom = list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"rebuilding geom for unit\",u)\n",
    "    geo = tractGeom[unitTractList[u][0]]\n",
    "    for b in unitTractList[u]:\n",
    "        if b != unitTractList[u][0]:\n",
    "            geo = geo.union(tractGeom[b])\n",
    "    unitGeom.append(geo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c5130866-007c-4d4f-b991-77cf96fdba33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "re-identify the border units.  Numbered units have zero nonborder neighbors; will be surrounded next block\n",
      "'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"re-identify the border units.  Numbered units have zero nonborder neighbors; will be surrounded next block\")\n",
    "print(\"'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for b in borderBGs:\n",
    "    u = tractUnitNo[b]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits.append(u)\n",
    "    if unitPop[u] > 0.5 * aDP:\n",
    "        plotCenter(\"pop\"+str(r3(unitPop[u]/aDP)),unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "295f8c1d-315f-4c3d-bcff-97a2506ae155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    if unitPop[u] > 0.77 * aDP and unitPop[u] < 0.78 * aDP:\n",
    "        plotPoly(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "48213b6e-2e5d-426a-a6b9-1be51b5cd438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state bgs = 9472 = 9472\n"
     ]
    }
   ],
   "source": [
    "print(\"state bgs =\",len(tractPop),\"=\",np.sum([np.sum([1 for b in unitTractList[u] ]) for u in range(nUnits)])  + len(skipList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "b4b7cab1-36b7-4885-834d-2a8e0a2c87dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "additional integrity checks -- bg's assigned to non-units\n"
     ]
    }
   ],
   "source": [
    "print(\"additional integrity checks -- bg's assigned to non-units\")\n",
    "for u in range(nUnits):\n",
    "    for bg in unitTractList[u]:\n",
    "        tractUnitNo[bg] = u\n",
    "        if tractUnitNo[bg] < 0:\n",
    "            print(\"based on tract unit list, bg\",bg,\"was assigned to non-unit\",u)\n",
    "for bg in range(nTracts):\n",
    "    if bg not in skipList and tractUnitNo[bg] < 0:\n",
    "        print(\"based on bg list, bg\",bg,\"was assigned to non-unit\",tractUnitNo[bg])\n",
    "        for u in range(nUnits):\n",
    "            if bg in unitTractList[u]:\n",
    "                tractUnitNo[bg] = 0\n",
    "                print(\" ... but I assigned bg\",bg,\"to unit\",u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b9993ece-c7c6-4dc6-8ff7-b0a3a4a3dac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do we have discontig units??\n"
     ]
    }
   ],
   "source": [
    "print(\"do we have discontig units??\")\n",
    "for u in range(nUnits):\n",
    "    if len(unitTractList[u]) > 1:\n",
    "        if not isContiguous(unitTractList[u],bgNbrs)[0]:\n",
    "            print(u,\"is not contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "730fa1c1-edf4-4718-99a6-8fdad2cf42b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-identify the border units.  Numbered units have no nonborder neighbors; would need to triage these again\n",
      "our new set of surroundedBorderUnits is []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Re-identify the border units.  Numbered units have no nonborder neighbors; would need to triage these again\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for b in borderBGs:\n",
    "    u = tractUnitNo[b]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits2 = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits2.append(u)\n",
    "print(\"our new set of surroundedBorderUnits is\",surroundedBorderUnits2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "94c70996-2188-479e-891f-28cb4b9beb1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check on NE Ohio\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]\n",
    "print(\"check on NE Ohio\")\n",
    "for u in borderUnits:\n",
    "    if unitCP[u].x > -81.2 and unitCP[u].y > 40.5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "9cd0f00e-4d16-49b9-a29c-b8f40455ba51",
   "metadata": {},
   "outputs": [],
   "source": [
    "keptLists = list()  #these will house the blockgroups retained in the border part of the irregular unit\n",
    "newsplitUs = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "98de7e45-962d-4f98-a4a6-bf219ae89e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are arbitrarily going to split some of these irregular units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"we are arbitrarily going to split some of these irregular units\")\n",
    "u = 941\n",
    "newsplitUs.append(u)\n",
    "newList = list()\n",
    "plotPoly(unitGeom[u])\n",
    "for b in unitTractList[u]:\n",
    "    if tractCP[b].x > -80.63 and tractCP[b].y > 40.88:\n",
    "        plotPoly(tractGeom[b],0.2)\n",
    "        plotCenter(b,tractGeom[b])\n",
    "        newList.append(b)\n",
    "plt.show()\n",
    "keptLists.append(newList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "b406af27-b9a6-4e63-807e-c3c385ccbee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are arbitrarily going to split some of these irregular units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"we are arbitrarily going to split some of these irregular units\")\n",
    "u = 3130\n",
    "newsplitUs.append(u)\n",
    "newList = list()\n",
    "plotPoly(unitGeom[u])\n",
    "for b in unitTractList[u]:\n",
    "    if tractCP[b].x > -81.02 :\n",
    "        plotPoly(tractGeom[b],0.2)\n",
    "        plotCenter(b,tractGeom[b])\n",
    "        newList.append(b)\n",
    "plt.show()\n",
    "keptLists.append(newList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "e311f7d3-dbd4-4faa-9397-ced4fc6a30b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[941, 3130] [[4413, 4415, 4443, 4414, 4412, 4417], [4738, 4686, 4688, 4739, 4745, 4684, 4685, 4687, 4689, 428, 4746, 4619, 4840, 5793, 4690, 4694, 5796, 4741, 4744, 4115, 5390, 5794, 1749, 4578, 1755, 4836, 4682, 4577, 174]]\n"
     ]
    }
   ],
   "source": [
    "print(newsplitUs,keptLists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "e90ba6ca-834f-43cf-9f6e-1ab9fea79a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "splitting these irregular units.  Prior to doing so, we have 3436 units\n",
      "Now we have 3438 units.  We have rebuilt the unitpops and geoms\n",
      "Update the reverse bg-unit assignments ...\n"
     ]
    }
   ],
   "source": [
    "oldList = [unitTractList[u].copy() for u in range(nUnits)] #safekeeping\n",
    "print(\"splitting these irregular units.  Prior to doing so, we have\",nUnits,\"units\")\n",
    "for i,u in enumerate(newsplitUs):\n",
    "    spawnedBGlist = list(set(oldList[u]).difference(set(keptLists[i])) )\n",
    "    unitTractList[u] = keptLists[i].copy()\n",
    "    unitTractList.append(spawnedBGlist)\n",
    "    unitPop[u] = np.sum([tractPop[b] for b in unitTractList[u]] )\n",
    "    unitGeom[u] = tractGeom[unitTractList[u][0]]\n",
    "    for b in unitTractList[u]:\n",
    "        unitGeom[u] = unitGeom[u].union(tractGeom[b])\n",
    "    unitPop.append(np.sum([tractPop[b] for b in spawnedBGlist]) )\n",
    "    unitGeom.append(tractGeom[spawnedBGlist[0]])\n",
    "    for b in spawnedBGlist:\n",
    "        unitGeom[-1] = unitGeom[-1].union(tractGeom[b])\n",
    "nUnits = len(unitTractList)\n",
    "print(\"Now we have\",nUnits,\"units.  We have rebuilt the unitpops and geoms\")\n",
    "\n",
    "print(\"Update the reverse bg-unit assignments ...\")\n",
    "for u in range(nUnits):\n",
    "    for b in unitTractList[u]:\n",
    "        tractUnitNo[b] = u\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "79271ce1-bbdf-4859-bbe0-9d40a7c6e3f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lists and pops updated.  Now the topology for all units\n",
      "build unit nbr list based on adjacent bg's\n",
      "state bgs = 9472 = 9472\n",
      "sum of unit areas = 11.460253084569997 vs map area 11.46025308457\n"
     ]
    }
   ],
   "source": [
    "print(\"lists and pops updated.  Now the topology for all units\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent bg's\")  #these are list-based, not geom-based\n",
    "for bg in range(nTracts):\n",
    "    u = tractUnitNo[bg]\n",
    "    for bb in bgNbrs[bg]:\n",
    "        uu = tractUnitNo[bb]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]\n",
    "print(\"state bgs =\",len(tractPop),\"=\",np.sum([np.sum([1 for b in unitTractList[u] ]) for u in range(nUnits)])  + len(skipList) )\n",
    "print(\"sum of unit areas =\",np.sum([unitGeom[u].area for u in range(nUnits)]),\"vs map area\",mainMAP.area + tractArea[Kelley])\n",
    "#main excludes Kelleys Island"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f60d1a6-7080-4463-acf8-ee81afefec8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "e0545c19-3336-4bad-9acf-5eb90106ff6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the unsplittable municipalities, between 50pct and 105% of a Congressional district\n",
      "449 86705 with first bg in county 56\n",
      "473 93181 with first bg in county 12\n",
      "587 67404 with first bg in county 85\n",
      "628 107417 with first bg in county 76\n",
      "728 83428 with first bg in county 48\n",
      "830 62509 with first bg in county 22\n",
      "1281 72438 with first bg in county 83\n",
      "1713 68756 with first bg in county 30\n",
      "1758 83114 with first bg in county 78\n",
      "2158 123272 with first bg in county 20\n",
      "2489 69428 with first bg in county 18\n",
      "3436 84336 with first bg in county 14\n",
      "above is out of a total of 3438 units\n"
     ]
    }
   ],
   "source": [
    "unitPop = [ np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits) ]\n",
    "unsplittableUnits = list()\n",
    "cantSplit = [0]*nUnits\n",
    "print(\"Here are the unsplittable municipalities, between 50pct and 105% of a Congressional district\") #Cincy, not Toledo\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] < 1.05*avgDistrictPop and unitPop[u] > 0.5 * avgDistrictPop:\n",
    "        unsplittableUnits.append(u)\n",
    "        cantSplit[u] = 1\n",
    "        print(u, r3(unitPop[u]),\"with first bg in county\",countyNo[unitTractList[u][0]])\n",
    "#print(\"and which ones are 0.2 - 0.35 aDP?\")\n",
    "#for u in range(nUnits):\n",
    "#    if unitPop[u] < 0.2*avgDistrictPop and unitPop[u] > 0.35 * avgDistrictPop:\n",
    "#        print(\"   \",u, r3(unitPop[u]),\"with first bg in county\",countyNo[unitTractList[u][0]] )\n",
    "print(\"above is out of a total of\",nUnits,\"units\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c8d2ae8-75dd-4c5e-8e65-2650fe1c0b3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "74e02028-4772-4612-9544-a7bc96f8850f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]\n",
    "plt.scatter([unitCP[u].x for u in range(nUnits)],unitPop/aDP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "dde025d5-86d9-4136-87a5-4dc28270d2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "minDistrictPop, maxDistrictPop = 0.95 * aDP, 1.05 * aDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "7254078e-b271-4790-b2f8-9820720e2f08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the set of whole units is {2158} with pops/aDP of [1.034]\n"
     ]
    }
   ],
   "source": [
    "wholeSet = set()\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > minDistrictPop and unitPop[u] < maxDistrictPop :\n",
    "        wholeSet.add(u)\n",
    "print(\"the set of whole units is\",wholeSet,\"with pops/aDP of\",[r3(unitPop[u]/aDP) for u in wholeSet])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "9971e41c-0c39-4bb2-9916-b20a4075f9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitCP = [unitGeom[u].centroid for u in range(nUnits) ]\n",
    "unitCPx = [unitCP[u].x for u in range(nUnits) ]\n",
    "unitCPy = [unitCP[u].y for u in range(nUnits) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "3c950093-cef8-43ca-ac68-6451cb06e858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "write the COI-legisl unit topology to a file ...\n"
     ]
    }
   ],
   "source": [
    "print(\"write the COI-legisl unit topology to a file ...\")\n",
    "date = \"12Dec\"\n",
    "nUnits = len(unitPop)\n",
    "unitNo = [u for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units\n",
    "\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"cantSplit\":cantSplit,\"unitNbrs\":unitNbrs, \"unitCountyNo\":unitCountyNo,\"unitTractList\":unitTractList} )\n",
    "outname = STATE+str(nDistricts)+\"COI1850-unitTopologiesBG_\"+date+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6007df08-67a8-42f3-b65b-145aeac2e050",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitPop[u] < 10:\n",
    "        print(u,\"has\",len(unitTractList[u]),\"bg's in it.  Total unit pop is \",unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0d963fd9-f506-4309-883a-6c8876af81c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pull in the any-split Home Districts.  These were based on VTDs, not blockgroups ...\n",
      "Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Pull in the any-split Home Districts.  These were based on VTDs, not blockgroups ...\")\n",
    "infile = \"./2024state_HD_output/OH8941VRApatchedVTDs.csv\"\n",
    "HDdf = pd.read_csv(infile)\n",
    "HDvtdListString, partialString, fracString = HDdf[\"HDvtdList\"], HDdf[\"partials\"],HDdf[\"HDvtdFrac\"]\n",
    "nHDs = len(HDvtdListString)\n",
    "HDCPx, HDCPy, HDwt = HDdf[\"centroid x\"],HDdf[\"centroid y\"], HDdf[\"HDweight\"]\n",
    "HDvTruePop = HDdf[\"HDvPop\"]\n",
    "HDCP = [Point(HDCPx[v], HDCPy[v]) for v in range(nHDs)]\n",
    "hdCP = HDCP.copy() #nome|nclature\n",
    "HDvtdList = [ast.literal_eval(HDvtdListString[h]) for h in range(nHDs)] #not needed if we use vtd--> bgLists below\n",
    "HDpartials = [ast.literal_eval(partialString[h]) for h in range(nHDs)]\n",
    "HDvtdFrac = [ast.literal_eval(fracString[h]) for h in range(nHDs)]\n",
    "print(\"Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\")\n",
    "for h in range(nHDs):\n",
    "    for i, frac in enumerate(HDvtdFrac[h]):\n",
    "        if frac >= 0.5:\n",
    "            HDvtdList[h].append(HDpartials[h][i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "400dac53-1036-4ff3-ae0c-bca26de36417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now read in the vtd geometry file, so we have the vtd centerpoints and pops\n"
     ]
    }
   ],
   "source": [
    "print(\"Now read in the vtd geometry file, so we have the vtd centerpoints and pops\")\n",
    "vtdPopFile = gpd.read_file(\"state_map_files/oh_pl2020_vtd.dbf\")\n",
    "vtdGeom = vtdPopFile['geometry']\n",
    "vtdPop = vtdPopFile['P0010001']\n",
    "nVTDs = len(vtdGeom)\n",
    "vtdArea = [vtdGeom[v].area for v in range(nVTDs)]\n",
    "vtdCP = [vtdGeom[v].centroid for v in range(nVTDs)]\n",
    "vtdCN = vtdPopFile['COUNTYFP20']\n",
    "vtdCountyNo = list()\n",
    "for v in range(nVTDs):\n",
    "    vtdCountyNo.append( int((int(vtdCN[v]) - 1)/2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "10d0c6ee-a37c-4ee4-b644-78f2d887b3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "isSkippedVTD = [0] *nVTDs  #this will house a temporary list of tracts for manipulation\n",
    "skippedVTDs = [8197, 4634, 3730, 4458, 840, 2974, 1008]\n",
    "for v in skippedVTDs:\n",
    "    isSkippedVTD[v] = 1\n",
    "countyVTDlist = [list() for c in range(nCounties)]\n",
    "for v in range(nVTDs):\n",
    "    if v not in skippedVTDs:\n",
    "        countyVTDlist[vtdCountyNo[v]].append(v)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ba668abf-7f0c-4d53-b2f9-d6b52f640930",
   "metadata": {},
   "source": [
    "####### improvement vs below - count the number of HD vtds in each county; if >0.99 * total, then include total"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d0fb9613-5677-42dd-9e9d-feb417aae8fb",
   "metadata": {},
   "source": [
    "#below is skipped because we can use the previously vtd --> bg snapping from another ipynb\n",
    "\n",
    "#HDvtdGeom = list()\n",
    "print(\"now we snap -- spatially determine HDgeom - blockgroup capture --> unitFracs\")\n",
    "popHDlist = list()\n",
    "startTime = time.time()\n",
    "unitFrac = [[0.]*nUnits for h in range(nHDs)]\n",
    "HDbgList = [list() for h in range(nHDs)]\n",
    "for h in range(nHDs):\n",
    "    if h%400 == 0:\n",
    "        print(\"building temporary HDvtdGeom for HD\",h,\"time is now\",int(time.time() - startTime))\n",
    "    if len(HDvtdList[h]) == 0:\n",
    "        #HDvtdGeom.append(dummyPoly)  #too much memory to store individually.\n",
    "        pass\n",
    "    else:\n",
    "        popHDlist.append(h)\n",
    "        #geo = vtdGeom[ HDvtdList[h][0] ]\n",
    "        HDcountyVTDs = [list() for c in range(nCounties)]\n",
    "        HDcountyGeo = [dummyPoly]*nCounties\n",
    "        capturedCounty = [0]*nCounties\n",
    "        for v in HDvtdList[h]:\n",
    "            c = vtdCountyNo[v]\n",
    "            HDcountyVTDs[c].append(v)\n",
    "        for c in range(nCounties):\n",
    "            if len(HDcountyVTDs[c]) > 0:  #some of county was HD-captured\n",
    "                if len(HDcountyVTDs[c]) >= 0.98 * len(countyVTDlist[c]) : #the HD essentially captured the county\n",
    "                    capturedCounty[c] = 1\n",
    "                    HDcountyGeo[c] = countyGeom[c]\n",
    "                else:\n",
    "                    HDcountyGeo[c] = vtdGeom[HDcountyVTDs[c][0]]\n",
    "                    for v in HDcountyVTDs[c]:\n",
    "                        HDcountyGeo[c] = HDcountyGeo[c].union(vtdGeom[v])\n",
    "\n",
    "        bList = list()\n",
    "        for c in range(nCounties):\n",
    "            if HDcountyGeo[c] != dummyPoly : #geo.intersects(countyGeom[c]):\n",
    "                if capturedCounty[c] == 1: #HDcountyGeo[c].contains(countyGeom[c]): #geo.contains(countyGeom[c]):\n",
    "                    for b in countyTractList[c]:\n",
    "                        bList.append(b)\n",
    "                else:\n",
    "                    for b in countyTractList[c]:\n",
    "                        if HDcountyGeo[c].contains(tractCP[b]): #geo.contains(tractCP[b]):\n",
    "                            bList.append(b)\n",
    "        HDbgList[h] = bList.copy()\n",
    "        for b in bList:\n",
    "            if b not in skipList and tractUnitNo[b] >= 0: #protect against unassigned 0-pop blockgroups\n",
    "                u = tractUnitNo[b]\n",
    "                if unitPop[tractUnitNo[b]] == 0:                       \n",
    "                    unitFrac[h][u] = 1.\n",
    "                else:\n",
    "                    unitFrac[h][u] += tractPop[b]/unitPop[u]\n",
    "        "
   ]
  },
  {
   "cell_type": "raw",
   "id": "69d0c42f-2a71-40c0-812a-8787a25e1aef",
   "metadata": {},
   "source": [
    "#again, skip this block #######################\n",
    "print(\"save this HDvtdList --> HDbgList to a file\") #useful block for COI615=COI1850\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"HD\":tList,\"HDweight\":HDwt,\"HDbgList\":HDbgList} )\n",
    "outname = STATE+str(int(nHDs))+\"VRAbgLists.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "0bbd7aff-06b3-4d6d-b771-7cd948369371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading in the previously stored HD bg lists, snapped vtd-based HDs to bg's in OH_615COIsnap99\n",
      "of the 8941 total HDs, 8934 are non-empty\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"reading in the previously stored HD bg lists, snapped vtd-based HDs to bg's in OH_615COIsnap99\")\n",
    "listDF = pd.read_csv(\"./2024state_HD_output/OH8941VRAbgLists.csv\")\n",
    "bgListString = listDF[\"HDbgList\"]\n",
    "HDwt = listDF[\"HDweight\"]\n",
    "HDweight = HDwt.copy()\n",
    "nHDs = len(bgListString)\n",
    "HDbgList = [ast.literal_eval(bgListString[h]) for h in range(nHDs) ]\n",
    "popHDlist = list()\n",
    "for h in range(nHDs):\n",
    "    if len(HDbgList[h]) > 0:\n",
    "        popHDlist.append(h)\n",
    "print(\"of the\",nHDs,\"total HDs,\",len(popHDlist),\"are non-empty\")\n",
    "\n",
    "unitFrac = [[0.]*nUnits for h in range(nHDs)]\n",
    "for h in popHDlist:\n",
    "    for b in HDbgList[h]:\n",
    "        if b not in skipList and tractUnitNo[b] >= 0: #protect against unassigned 0-pop blockgroups\n",
    "            u = tractUnitNo[b]\n",
    "            if unitPop[tractUnitNo[b]] == 0:                       \n",
    "                unitFrac[h][u] = 1.\n",
    "            else:\n",
    "                unitFrac[h][u] += tractPop[b]/unitPop[u]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "1fbef9d8-35f8-49cf-bb3e-6d6acf517033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 119186.343 9472 3438\n",
      "35\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, r3(aDP), nTracts, nUnits)\n",
    "print(np.min(unitPop))\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units\n",
    "for u in range(nUnits):\n",
    "    countyUnitList[unitCountyNo[u]].append(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "6d5085d1-1dd0-465c-98f4-d4769f5eadde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In this block, define each HDcp's home unit. Next block will then build unit-based HDs out from home muni...\n",
      "since units are blockgroup based, we assign HD centerpoints to the unit that contains it\n",
      "determining homeU for HD 0 time is now 0\n",
      "determining homeU for HD 1000 time is now 15\n",
      "determining homeU for HD 2000 time is now 21\n",
      "determining homeU for HD 3000 time is now 37\n",
      "determining homeU for HD 4000 time is now 42\n",
      "determining homeU for HD 5000 time is now 54\n",
      "determining homeU for HD 6000 time is now 61\n",
      "determining homeU for HD 7000 time is now 73\n",
      "determining homeU for HD 8000 time is now 80\n"
     ]
    }
   ],
   "source": [
    "print(\"In this block, define each HDcp's home unit. Next block will then build unit-based HDs out from home muni...\")\n",
    "minSnapFrac, minEligFrac = 0.667, 0.1 #we auto-add contig munis at least this full in HDvtdList\n",
    "homeU, HDvPop, HDunitList = [-888]*nHDs, [0.]*nHDs, [list() for h in range(nHDs) ]\n",
    "print(\"since units are blockgroup based, we assign HD centerpoints to the unit that contains it\")\n",
    "homeU, homeC = [-999]*nHDs, [-999]*nHDs\n",
    "startTime = time.time()\n",
    "for h in popHDlist:\n",
    "    if h%1000 == 0:\n",
    "        print(\"determining homeU for HD\",h,\"time is now\",int(time.time()-startTime))\n",
    "    for c in range(nCounties):\n",
    "        if countyGeom[c].contains(HDCP[h]):\n",
    "            homeC[h] = c\n",
    "            break\n",
    "    if homeC[h] < 0:  #not found inside a county; assign to closest\n",
    "        cDist = [HDCP[h].distance(countyGeom[c]) for c in range(nCounties)]\n",
    "        homeC[h] = cDist.index(np.min(cDist))\n",
    "    for u in countyUnitList[c]:\n",
    "        if unitGeom[u].contains(HDCP[h]):\n",
    "            homeU[h] = u\n",
    "            break        \n",
    "    if homeU[h] < 0:  #HDCP not found inside a unit; assign to closest among ALL units\n",
    "        uDist = [HDCP[h].distance(unitGeom[u]) for u in range(nUnits)]\n",
    "        homeU[h] = uDist.index(np.min(uDist))\n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9fe8940f-434d-405d-b52f-e17074715fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... we add units that were partially included, biasing toward close, mostly full ones\n",
      "working on snapping HD 0 in unit 2124 to whole units; time is 0\n",
      "  we've found a total of 0 HDs that had low contig-unit pops\n",
      "working on snapping HD 2002 in unit 414 to whole units; time is 3\n",
      "  we've found a total of 0 HDs that had low contig-unit pops\n",
      "working on snapping HD 4004 in unit 702 to whole units; time is 7\n",
      "  we've found a total of 0 HDs that had low contig-unit pops\n",
      "working on snapping HD 6006 in unit 33 to whole units; time is 11\n",
      "  we've found a total of 0 HDs that had low contig-unit pops\n",
      "working on snapping HD 8006 in unit 587 to whole units; time is 15\n",
      "  we've found a total of 0 HDs that had low contig-unit pops\n"
     ]
    }
   ],
   "source": [
    "# LEVERAGE FRACS PREVIOUSLY DETERMINED \n",
    "print(\"... we add units that were partially included, biasing toward close, mostly full ones\")\n",
    "startTime = time.time()\n",
    "lowContigPopHDs,lowContigPops = list(), list()\n",
    "for nh,h in enumerate(popHDlist):\n",
    "    origL = ( np.sum([vtdArea[v] for v in HDvtdList[h] ] ) )**0.5\n",
    "    if nh%2000 == 0:\n",
    "        print(\"working on snapping HD\",h,\"in unit\",homeU[h],\"to whole units; time is\",int(time.time()-startTime) )\n",
    "        print(\"  we've found a total of\",len(lowContigPops),\"HDs that had low contig-unit pops\")\n",
    "    currPop, addedUset = unitPop[homeU[h]], { homeU[h] }\n",
    "    if homeU[h] not in wholeSet:  #i.e. HDCP not in a full-district unit, so we can add more\n",
    "        useFrac = [unitFrac[h][u] for u in range(nUnits) ] #[0.]*nUnits\n",
    "        #for v in HDvtdList[h]:\n",
    "        #    useFrac[homeU[v]] += tractPop[v] / unitPop[homeU[v]]\n",
    "        shouldAddUset, hasFracUset = set(), set()\n",
    "        for u in range(nUnits):\n",
    "            if useFrac[u] > minSnapFrac and u not in wholeSet:  #munis more than 2/3 captured should be snapped if contig path found\n",
    "                shouldAddUset.add(u)\n",
    "            if useFrac[u] > minEligFrac  and u not in wholeSet :  #munis that were at least 10% captured are eligible to be snapped\n",
    "                hasFracUset.add(u)\n",
    "        #print(\"the len of the shouldAddUset and hasFracUset are\",len(shouldAddUset),len(hasFracUset) )\n",
    "        shouldAddUset = shouldAddUset.difference(addedUset)  #at this point, this knocks out just the home muni\n",
    "        nJustAdded = 1\n",
    "        while nJustAdded > 0 and currPop < aDP:  #this block -- add all mostly full HDs that are contig to growing list\n",
    "            nJustAdded = 0\n",
    "            canAddUset = set()\n",
    "            for u in addedUset:  #building all neighbors of in-HD munis\n",
    "                canAddUset = canAddUset.union(set(unitNbrs[u]).difference(wholeSet))\n",
    "            canAddUset = canAddUset.intersection( shouldAddUset.difference(addedUset) ) #whittling to eligible list\n",
    "            for u in canAddUset:\n",
    "                if currPop + unitPop[u] < 1.05 * aDP: #prevent big overadds\n",
    "                    currPop += unitPop[u]\n",
    "                    addedUset.add(u)\n",
    "                    nJustAdded +=1\n",
    "                if currPop > 0.95 * aDP:  #it's OK if we go way over; we'll trim back later\n",
    "                    break\n",
    "        # OK, we've added the 2/3 full munis.  Now add more contiguous partials if needed\n",
    "        eligibleSet = (hasFracUset.difference(addedUset)).difference(wholeSet)\n",
    "        for u in list(eligibleSet):\n",
    "            if currPop + 0.5 * unitPop[u] > 1.01 * aDP:\n",
    "                eligibleSet.remove(u)\n",
    "        contigAddableSet = set()  #the eligible units adjacent to the current (growing) set of units\n",
    "        for u in addedUset:\n",
    "            contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "        while currPop < 0.99 * aDP and len(contigAddableSet) > 0:  #add close, mostly used munis\n",
    "            canAddUlist = list(contigAddableSet)\n",
    "            canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "            addIdx = canAddScores.index(np.min(canAddScores))\n",
    "            u = canAddUlist[addIdx]\n",
    "            currPop += unitPop[u]\n",
    "            addedUset.add(u)\n",
    "            contigAddableSet.remove(u)\n",
    "            eligibleSet.remove(u)\n",
    "            contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "            for u in list(contigAddableSet):\n",
    "                if currPop + 0.5 * unitPop[u] > 1.01 * aDP: #prevent big overadds\n",
    "                    contigAddableSet.remove(u)\n",
    "        if currPop < 0.2 * aDP:\n",
    "            lowContigPopHDs.append(h)\n",
    "            lowContigPops.append(currPop)\n",
    "            #print(\"HD\",h,\" has only\",r3(currPop/aDP),\"pop/aDP. We will swell by nearest units to shore it up\")\n",
    "            canAddUset = set(unitNbrs[homeU[h]])\n",
    "            for u in addedUset:\n",
    "                canAddUset = canAddUset.intersection(unitNbrs[u])\n",
    "            canAddUset = ( canAddUset.difference(addedUset) ).difference(wholeSet)\n",
    "            while currPop < 0.99 * aDP and len(canAddUset) > 0:  #add close munis, biasing towards highly used\n",
    "                canAddUlist = list(canAddUset)\n",
    "                canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "                addIdx = canAddScores.index(np.min(canAddScores))\n",
    "                u = canAddUlist[addIdx]\n",
    "                currPop += unitPop[u]\n",
    "                addedUset.add(u)\n",
    "                canAddUset.remove(u)\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if useFrac[uu] > 0 and uu not in addedUset.union(wholeSet):\n",
    "                        canAddUset.add(uu)\n",
    "                for u in list(canAddUset):\n",
    "                    if currPop + 0.5 * unitPop[u] > 1.01 * aDP: #prevent big overadds\n",
    "                        canAddUset.remove(u)\n",
    "\n",
    "    HDvPop[h] = currPop\n",
    "    HDunitList[h] = list(addedUset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "7ccd72b7-92a1-49b9-9c47-f326577e107f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for h in popHDlist:\n",
    "    if homeU[h] < 0:\n",
    "        plotPoly(HDCP[h].buffer(0.1))\n",
    "        plotPoly(countyGeom[homeC[h]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "6ca0e286-f4bc-4153-8e53-42e5b6757339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets plot our unit-snapped HD pops vs. orig pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and compute current unit usage with these whole-unit, possibly discontig HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets plot our unit-snapped HD pops vs. orig pops\")\n",
    "plt.scatter([HDvTruePop[h] for h in popHDlist], [HDvPop[h] for h in popHDlist])\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"and compute current unit usage with these whole-unit, possibly discontig HDs\")\n",
    "unitUse = [0.]*nUnits\n",
    "for h in popHDlist:\n",
    "    for u in HDunitList[h]:\n",
    "        unitUse[u] += nDistricts * HDwt[h]\n",
    "plt.hist(unitUse,weights=unitPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "81a441d0-3a2f-473d-ba3b-0c8b365dfb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "ecceb61d-a30b-4337-81cd-75fb7ef961af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the severely underused units, <0.5 use\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the severely underused units, <0.5 use\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(mainMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "30de9de6-e831-4cfa-a449-2cc7bf3d330f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify these unit HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 46\n",
      "working on HD 1402 time is now 68\n",
      "working on HD 2102 time is now 86\n",
      "working on HD 2802 time is now 123\n",
      "working on HD 3503 time is now 145\n",
      "working on HD 4204 time is now 165\n",
      "working on HD 4906 time is now 195\n",
      "working on HD 5606 time is now 226\n",
      "working on HD 6306 time is now 264\n",
      "working on HD 7006 time is now 306\n",
      "working on HD 7706 time is now 339\n",
      "working on HD 8407 time is now 365\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 2311.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 6623 and discontig only= 0\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 0 6623\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"classify these unit HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "6421f8ab-a699-4c29-976e-8c8e39d4597a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 131104 district pop vs 119186 target\n",
      "working on enclave-y HD 0 . We have evaluated 0 of 6623 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 582 . We have evaluated 441 of 6623 enclavy HDs.Time is now 9\n",
      "working on enclave-y HD 1139 . We have evaluated 882 of 6623 enclavy HDs.Time is now 15\n",
      "working on enclave-y HD 1673 . We have evaluated 1323 of 6623 enclavy HDs.Time is now 20\n",
      "working on enclave-y HD 2237 . We have evaluated 1764 of 6623 enclavy HDs.Time is now 25\n",
      "working on enclave-y HD 2828 . We have evaluated 2205 of 6623 enclavy HDs.Time is now 33\n",
      "working on enclave-y HD 3362 . We have evaluated 2646 of 6623 enclavy HDs.Time is now 39\n",
      "working on enclave-y HD 3901 . We have evaluated 3087 of 6623 enclavy HDs.Time is now 44\n",
      "working on enclave-y HD 4448 . We have evaluated 3528 of 6623 enclavy HDs.Time is now 50\n",
      "working on enclave-y HD 5069 . We have evaluated 3969 of 6623 enclavy HDs.Time is now 56\n",
      "working on enclave-y HD 5776 . We have evaluated 4410 of 6623 enclavy HDs.Time is now 64\n",
      "working on enclave-y HD 6359 . We have evaluated 4851 of 6623 enclavy HDs.Time is now 72\n",
      "working on enclave-y HD 6975 . We have evaluated 5292 of 6623 enclavy HDs.Time is now 81\n",
      "working on enclave-y HD 7661 . We have evaluated 5733 of 6623 enclavy HDs.Time is now 89\n",
      "working on enclave-y HD 8353 . We have evaluated 6174 of 6623 enclavy HDs.Time is now 95\n",
      "working on enclave-y HD 8932 . We have evaluated 6615 of 6623 enclavy HDs.Time is now 102\n",
      "Out of 6623 HDs with enclaves 6623 had enclaves.\n",
      "Of these, 5183 wouldn't be over 131104 if all enclaves filled, while 1440 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copy enclave fix here.  Will not have any muni-discontig\n",
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.1 * aDP) #wider tolerance here for unit vs. vtd-based, but still Congr'l\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",int(maxPostFixPop),\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(eLgenerator):\n",
    "    if iii%(int(len(eLgenerator)/15)) == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(eLgenerator),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "aced004d-abcf-4b9d-8249-332156a1759a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.03225 0.50991\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "HDweight = HDwt.copy() #nomenclature\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.01:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "c06c0980-fad8-4596-bf2f-ad46bd6bb074",
   "metadata": {},
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "80050c17-7680-4ea9-b803-dfc5bc057e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d7231cb1-7b57-4509-bbc6-44ca5b789c0d",
   "metadata": {},
   "source": [
    "##run this only if we restarted ............\n",
    "print(\"classify these unit HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2cde5062-e039-437b-86fe-2f3971cef22e",
   "metadata": {},
   "source": [
    "print(\"run this only if we restarted\")\n",
    "cantFill = eLgenerator.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "827b8496-d66d-40ae-9780-a20243a54f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 1440 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u],0.1)\n",
    "for t in cantFill:\n",
    "    plotPoly(HDCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "d4eec53d-e431-4e8d-a430-31ef4d1aa8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1602\n",
      "working on avg unit-to-HDcp dist for HD 2402\n",
      "working on avg unit-to-HDcp dist for HD 3203\n",
      "working on avg unit-to-HDcp dist for HD 4004\n",
      "working on avg unit-to-HDcp dist for HD 4806\n",
      "working on avg unit-to-HDcp dist for HD 5606\n",
      "working on avg unit-to-HDcp dist for HD 6406\n",
      "working on avg unit-to-HDcp dist for HD 7206\n",
      "working on avg unit-to-HDcp dist for HD 8006\n",
      "working on avg unit-to-HDcp dist for HD 8807\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([]) #for basic muni snap, there are no special corner units to freeze\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "76225203-dcf1-4845-b6a2-3b75e3749b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for this code, we don't have any clusters\n"
     ]
    }
   ],
   "source": [
    "print(\"for this code, we don't have any clusters\")\n",
    "allUnits = [u for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "3c7dd06e-0f40-4f55-a421-3400f43d6a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I skipped this for OH_1850COIsnap15, not working right\n",
      "this MUSTFREE code doesn't work right if a set of border units is surrounded by other border units\n",
      "this code will solve large enclaves so HD and complement are contiguous for 1440 HDs\n",
      "working on HD 7 cantFill 0 of 1440 .Time is now 0\n",
      "working on HD 83 cantFill 20 of 1440 .Time is now 0\n",
      "working on HD 199 cantFill 40 of 1440 .Time is now 0\n",
      "working on HD 260 cantFill 60 of 1440 .Time is now 0\n",
      "working on HD 301 cantFill 80 of 1440 .Time is now 0\n",
      "working on HD 345 cantFill 100 of 1440 .Time is now 0\n",
      "working on HD 592 cantFill 120 of 1440 .Time is now 0\n",
      "working on HD 654 cantFill 140 of 1440 .Time is now 0\n",
      "working on HD 719 cantFill 160 of 1440 .Time is now 1\n",
      "working on HD 918 cantFill 180 of 1440 .Time is now 1\n",
      "working on HD 965 cantFill 200 of 1440 .Time is now 1\n",
      "working on HD 1046 cantFill 220 of 1440 .Time is now 1\n",
      "working on HD 1146 cantFill 240 of 1440 .Time is now 1\n",
      "giving up for HD 1213 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1252 cantFill 260 of 1440 .Time is now 1\n",
      "giving up for HD 1253 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1291 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1339 cantFill 280 of 1440 .Time is now 1\n",
      "giving up for HD 1344 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1380 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1387 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1423 cantFill 300 of 1440 .Time is now 1\n",
      "giving up for HD 1471 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1496 cantFill 320 of 1440 .Time is now 1\n",
      "giving up for HD 1529 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1533 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1554 cantFill 340 of 1440 .Time is now 1\n",
      "giving up for HD 1601 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1611 cantFill 360 of 1440 .Time is now 1\n",
      "working on HD 1697 cantFill 380 of 1440 .Time is now 1\n",
      "giving up for HD 1738 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 1918 cantFill 400 of 1440 .Time is now 2\n",
      "giving up for HD 1931 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1932 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1933 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1935 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1936 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1937 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1938 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1945 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1950 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1962 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1989 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 1992 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2012 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2014 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2015 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2017 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2023 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2047 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 2081 cantFill 420 of 1440 .Time is now 2\n",
      "giving up for HD 2081 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 2141 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 2273 cantFill 440 of 1440 .Time is now 2\n",
      "working on HD 2439 cantFill 460 of 1440 .Time is now 2\n",
      "working on HD 2553 cantFill 480 of 1440 .Time is now 2\n",
      "working on HD 2701 cantFill 500 of 1440 .Time is now 2\n",
      "working on HD 2784 cantFill 520 of 1440 .Time is now 2\n",
      "working on HD 2834 cantFill 540 of 1440 .Time is now 2\n",
      "working on HD 2959 cantFill 560 of 1440 .Time is now 3\n",
      "working on HD 3051 cantFill 580 of 1440 .Time is now 3\n",
      "working on HD 3145 cantFill 600 of 1440 .Time is now 3\n",
      "working on HD 3221 cantFill 620 of 1440 .Time is now 3\n",
      "working on HD 3419 cantFill 640 of 1440 .Time is now 3\n",
      "working on HD 3533 cantFill 660 of 1440 .Time is now 3\n",
      "working on HD 3823 cantFill 680 of 1440 .Time is now 3\n",
      "working on HD 3927 cantFill 700 of 1440 .Time is now 3\n",
      "working on HD 4074 cantFill 720 of 1440 .Time is now 3\n",
      "working on HD 4266 cantFill 740 of 1440 .Time is now 3\n",
      "working on HD 4329 cantFill 760 of 1440 .Time is now 3\n",
      "working on HD 4446 cantFill 780 of 1440 .Time is now 4\n",
      "working on HD 4479 cantFill 800 of 1440 .Time is now 4\n",
      "working on HD 4556 cantFill 820 of 1440 .Time is now 4\n",
      "working on HD 4669 cantFill 840 of 1440 .Time is now 4\n",
      "working on HD 4770 cantFill 860 of 1440 .Time is now 4\n",
      "giving up for HD 4771 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 4792 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 4849 cantFill 880 of 1440 .Time is now 4\n",
      "working on HD 4959 cantFill 900 of 1440 .Time is now 4\n",
      "giving up for HD 4984 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 4985 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 5008 cantFill 920 of 1440 .Time is now 4\n",
      "giving up for HD 5077 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 5078 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 5086 cantFill 940 of 1440 .Time is now 4\n",
      "working on HD 5165 cantFill 960 of 1440 .Time is now 4\n",
      "giving up for HD 5571 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 5620 cantFill 980 of 1440 .Time is now 5\n",
      "giving up for HD 5620 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 5632 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 5634 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 5761 cantFill 1000 of 1440 .Time is now 5\n",
      "working on HD 5854 cantFill 1020 of 1440 .Time is now 5\n",
      "working on HD 5977 cantFill 1040 of 1440 .Time is now 5\n",
      "working on HD 6176 cantFill 1060 of 1440 .Time is now 5\n",
      "working on HD 6235 cantFill 1080 of 1440 .Time is now 5\n",
      "working on HD 6313 cantFill 1100 of 1440 .Time is now 5\n",
      "working on HD 6396 cantFill 1120 of 1440 .Time is now 5\n",
      "working on HD 6496 cantFill 1140 of 1440 .Time is now 5\n",
      "working on HD 6575 cantFill 1160 of 1440 .Time is now 6\n",
      "working on HD 6650 cantFill 1180 of 1440 .Time is now 6\n",
      "working on HD 6820 cantFill 1200 of 1440 .Time is now 6\n",
      "working on HD 7039 cantFill 1220 of 1440 .Time is now 6\n",
      "working on HD 7238 cantFill 1240 of 1440 .Time is now 6\n",
      "working on HD 7445 cantFill 1260 of 1440 .Time is now 6\n",
      "working on HD 7588 cantFill 1280 of 1440 .Time is now 6\n",
      "working on HD 7780 cantFill 1300 of 1440 .Time is now 6\n",
      "working on HD 8030 cantFill 1320 of 1440 .Time is now 6\n",
      "giving up for HD 8126 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8127 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8128 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8130 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8138 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8143 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8147 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8181 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8182 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8183 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8187 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8189 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8191 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8194 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8195 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8196 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8212 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 8216 cantFill 1340 of 1440 .Time is now 6\n",
      "giving up for HD 8216 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8227 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8228 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8229 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8230 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8231 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8232 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8251 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8252 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8254 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8256 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8262 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8263 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8264 there are only 1 freeing units for 1 enclaves\n",
      "giving up for HD 8265 there are only 1 freeing units for 1 enclaves\n",
      "working on HD 8370 cantFill 1360 of 1440 .Time is now 6\n",
      "working on HD 8463 cantFill 1380 of 1440 .Time is now 7\n",
      "working on HD 8606 cantFill 1400 of 1440 .Time is now 7\n",
      "working on HD 8762 cantFill 1420 of 1440 .Time is now 7\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.0455 0.49558\n"
     ]
    },
    {
     "data": {
      "image/png": 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aL/Xqorc/pD1Pd/aEtB8AAOMFwWYUtV/qlXPTIfUODIa8d0JsjKZMdIS8LwAAYxnBZhRd9Pard2BQNWW5mpkyKaS9p0x0KCM5IaQ9AQAY6wg2Y8DMlEnKzkga7WEAADDucfIwAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYwwr2NTW1iorK0vx8fEqLCzU0aNHr1u/e/duzZkzR/Hx8brvvvu0f//+a2ref/99ffOb31RSUpImTpyo/Px8nT17djjDAwAAEcp2sKmvr5fL5dL69evV2tqqnJwclZSUqLOzM2j94cOHtWjRIj355JM6fvy4SktLVVpaqhMnTvhrPvjgAz388MOaM2eOmpqa9M4772jt2rWKj48f/pYBAICIE2VZlmVnhcLCQuXn52vr1q2SJJ/Pp8zMTC1btkyrVq26pr6srExer1f79u3zL3vwwQeVm5ururo6SdITTzyh2NhY/exnPxvWRnR3dyspKUldXV1KTEwcVo/RcKK9S49veUv7lj2s7Iyk0R4OcEvYnwHYFY7Pb1vf2PT396ulpUVOp/OLBtHRcjqdam5uDrpOc3NzQL0klZSU+Ot9Pp9ef/11ffWrX1VJSYlSUlJUWFiovXv32twUAAAQ6WwFmwsXLmhwcFCpqakBy1NTU+XxeIKu4/F4rlvf2dmpnp4ebdiwQY8++qh+9atf6dvf/ra+853v6NChQ0F79vX1qbu7O+ABAAAwYbQH4PP5JEnf+ta39Dd/8zeSpNzcXB0+fFh1dXUqLi6+Zh23261nn312RMcJAADGPlvf2EydOlUxMTHq6OgIWN7R0aG0tLSg66SlpV23furUqZowYYLmzp0bUHPPPfcM+auo6upqdXV1+R/nzp2zsxkAAMBQtoKNw+FQXl6eGhsb/ct8Pp8aGxtVVFQUdJ2ioqKAekk6cOCAv97hcCg/P1+nTp0KqPnd736n6dOnB+0ZFxenxMTEgAcAAIDtQ1Eul0vl5eWaN2+eCgoKVFNTI6/Xq4qKCknSkiVLlJGRIbfbLUlavny5iouLtWnTJi1cuFC7du3SsWPHtG3bNn/PlStXqqysTI888oi+/vWvq6GhQf/5n/+ppqam0GwlAACICLaDTVlZmc6fP69169bJ4/EoNzdXDQ0N/hOEz549q+joL74Imj9/vnbu3Kk1a9Zo9erVmjVrlvbu3avs7Gx/zbe//W3V1dXJ7XbrRz/6kWbPnq1XX31VDz/8cAg2EQAARArb17EZi7iODTD62J8B2DXq17EBAAAYywg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIwxYbQHAMAspzt7Qt5zykSHMpITQt4XgHkINgBCYspEhxJiY1RV3xby3gmxMTq4ophwA+CGCDYAQiIjOUEHVxTrorc/pH1Pd/aoqr5NF739BBsAN0SwARAyGckJhA8Ao4qThwEAgDGGFWxqa2uVlZWl+Ph4FRYW6ujRo9et3717t+bMmaP4+Hjdd9992r9//5C1zzzzjKKiolRTUzOcoQEAgAhmO9jU19fL5XJp/fr1am1tVU5OjkpKStTZ2Rm0/vDhw1q0aJGefPJJHT9+XKWlpSotLdWJEyeuqd2zZ4+OHDmi9PR0+1sCAAAinu1gs3nzZj311FOqqKjQ3LlzVVdXp9tuu03bt28PWv/SSy/p0Ucf1cqVK3XPPffoueee0wMPPKCtW7cG1LW3t2vZsmX6t3/7N8XGxg5vawAAQESzFWz6+/vV0tIip9P5RYPoaDmdTjU3Nwddp7m5OaBekkpKSgLqfT6fFi9erJUrV+ree++94Tj6+vrU3d0d8AAAALAVbC5cuKDBwUGlpqYGLE9NTZXH4wm6jsfjuWH9888/rwkTJuhHP/rRTY3D7XYrKSnJ/8jMzLSzGQAAwFCj/quolpYWvfTSS9qxY4eioqJuap3q6mp1dXX5H+fOnQvzKAEAwHhgK9hMnTpVMTEx6ujoCFje0dGhtLS0oOukpaVdt/6///u/1dnZqbvuuksTJkzQhAkTdObMGa1YsUJZWVlBe8bFxSkxMTHgAQAAYOsCfQ6HQ3l5eWpsbFRpaamkz8+PaWxsVGVlZdB1ioqK1NjYqKqqKv+yAwcOqKioSJK0ePHioOfgLF68WBUVFXaGB8Bg4bgHlcR9qADT2L7ysMvlUnl5uebNm6eCggLV1NTI6/X6Q8iSJUuUkZEht9stSVq+fLmKi4u1adMmLVy4ULt27dKxY8e0bds2SdIdd9yhO+64I+BvxMbGKi0tTbNnz77V7QMwzoXzHlQS96ECTGM72JSVlen8+fNat26dPB6PcnNz1dDQ4D9B+OzZs4qO/uII1/z587Vz506tWbNGq1ev1qxZs7R3715lZ2eHbisAGCtc96CSuA8VYKJh3SuqsrJyyENPTU1N1yz73ve+p+9973s33f/jjz8ezrAAGIp7UAG4WaP+qygAAIBQIdgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxpgw2gMAgNF2urMnLH2nTHQoIzkhLL0BBEewARCxpkx0KCE2RlX1bWHpnxAbo4Mrigk3wAgaVrCpra3VCy+8II/Ho5ycHG3ZskUFBQVD1u/evVtr167Vxx9/rFmzZun555/XY489JkkaGBjQmjVrtH//fn344YdKSkqS0+nUhg0blJ6ePrytAoCbkJGcoIMrinXR2x/y3qc7e1RV36aL3n6CDTCCbAeb+vp6uVwu1dXVqbCwUDU1NSopKdGpU6eUkpJyTf3hw4e1aNEiud1uPf7449q5c6dKS0vV2tqq7OxsXblyRa2trVq7dq1ycnJ08eJFLV++XN/85jd17NixkGwkAAwlIzmB4AEYJMqyLMvOCoWFhcrPz9fWrVslST6fT5mZmVq2bJlWrVp1TX1ZWZm8Xq/27dvnX/bggw8qNzdXdXV1Qf/G22+/rYKCAp05c0Z33XXXDcfU3d2tpKQkdXV1KTEx0c7mjKoT7V16fMtb2rfsYWVnJI32cACEEP9/AzcWjs9vW7+K6u/vV0tLi5xO5xcNoqPldDrV3NwcdJ3m5uaAekkqKSkZsl6Surq6FBUVpeTk5KCv9/X1qbu7O+ABAABgK9hcuHBBg4ODSk1NDViempoqj8cTdB2Px2Or/v/+7//0d3/3d1q0aNGQ6c3tdispKcn/yMzMtLMZAADAUGPqOjYDAwP6/ve/L8uy9PLLLw9ZV11dra6uLv/j3LlzIzhKAAAwVtk6eXjq1KmKiYlRR0dHwPKOjg6lpaUFXSctLe2m6q+GmjNnzuiNN9647rG2uLg4xcXF2Rk6AACIALa+sXE4HMrLy1NjY6N/mc/nU2Njo4qKioKuU1RUFFAvSQcOHAiovxpqfv/73+vgwYO644477AwLAABA0jB+7u1yuVReXq558+apoKBANTU18nq9qqiokCQtWbJEGRkZcrvdkqTly5eruLhYmzZt0sKFC7Vr1y4dO3ZM27Ztk/R5qPnzP/9ztba2at++fRocHPSff3P77bfL4XCEalsBYMSF46rGXNEYGJrtYFNWVqbz589r3bp18ng8ys3NVUNDg/8E4bNnzyo6+osvgubPn6+dO3dqzZo1Wr16tWbNmqW9e/cqOztbktTe3q7XXntNkpSbmxvwt379619rwYIFw9w0ABg94byqMVc0BoZm+zo2YxHXsQEwFrVf6g35VY2vXtGY9w2YIByf39wrCgDChKsaAyNvTP3cGwAA4FYQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjMF1bG5COC6yJYXnUusAAEQygs0NtF/qlXPTIfUODIalf0JsjKZM5H5YAACEAsHmBi56+9U7MKiaslzNTJkU8v7czA4AgNAh2NykmSmTuC8LAABjHCcPAwAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIwxYbQHAACw73RnT1j6TpnoUEZyQlh6AyOBYAMA48iUiQ4lxMaoqr4tLP0TYmN0cEUx4QbjFsEGAMaRjOQEHVxRrIve/pD3Pt3Zo6r6Nl309hNsMG4RbABgnMlITghr8AjHYS4OcWGkEGwAAJLCe5iLQ1wYKQQbAICk8B3m4hAXRhLBBgDgF+7DXEC4cR0bAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABj8HNvAMC41n6pNyy3mOBqyeMTwQYAMG61X+qVc9Mh9Q4Mhrw3V0senwg2AIAREY57UJ3u7FHvwKBqynI1M2VSSPtyteTxaVjBpra2Vi+88II8Ho9ycnK0ZcsWFRQUDFm/e/durV27Vh9//LFmzZql559/Xo899pj/dcuytH79ev30pz/VpUuX9NBDD+nll1/WrFmzhjM8AMAYEs57UEmff7OSP+P2sASQcIQxicNc4WQ72NTX18vlcqmurk6FhYWqqalRSUmJTp06pZSUlGvqDx8+rEWLFsntduvxxx/Xzp07VVpaqtbWVmVnZ0uS/vEf/1H/9E//pH/5l3/RjBkztHbtWpWUlOjkyZOKj4+/9a0EAIyacN2D6qpwhISRCGPhOMwVrvONpPETxqIsy7LsrFBYWKj8/Hxt3bpVkuTz+ZSZmally5Zp1apV19SXlZXJ6/Vq3759/mUPPvigcnNzVVdXJ8uylJ6erhUrVuhv//ZvJUldXV1KTU3Vjh079MQTT9xwTN3d3UpKSlJXV5cSExPtbM4NnWjv0uNb3tK+ZQ8rOyMppL0BAGNXuELC1cNcof5cCef5RlJ4wlg4Pr9tfWPT39+vlpYWVVdX+5dFR0fL6XSqubk56DrNzc1yuVwBy0pKSrR3715J0kcffSSPxyOn0+l/PSkpSYWFhWpubg4abPr6+tTX1+d/3tXVJenzCQq1nsvd8vVdUc/lbnV3R4W8PwBgbJocLU2eHPr3/Z7LPvn6ruidDz9Vz+XQfW59eN4rb89lbfjOfbr7KxND1vdq71X/712d81zQ5OjQhbGrn9s2v2O5LlvB5sKFCxocHFRqamrA8tTUVP32t78Nuo7H4wla7/F4/K9fXTZUzZe53W49++yz1yzPzMy8uQ0ZhqKasLUGAESgH9SMr75S+D4LL1++rKSk0ASmcfmrqOrq6oBvgXw+nz777DPdcccdiooKTNfd3d3KzMzUuXPnQn6YajxjXq7FnATHvATHvATHvATHvFzr6pycPHlS6enpIetrK9hMnTpVMTEx6ujoCFje0dGhtLS0oOukpaVdt/7qPzs6OnTnnXcG1OTm5gbtGRcXp7i4uIBlycnJ1x17YmIiO1MQzMu1mJPgmJfgmJfgmJfgmJdrZWRkKDo6dDdCsNXJ4XAoLy9PjY2N/mU+n0+NjY0qKioKuk5RUVFAvSQdOHDAXz9jxgylpaUF1HR3d+t//ud/huwJAAAQjO1DUS6XS+Xl5Zo3b54KCgpUU1Mjr9eriooKSdKSJUuUkZEht9stSVq+fLmKi4u1adMmLVy4ULt27dKxY8e0bds2SVJUVJSqqqr093//95o1a5b/597p6ekqLS0N3ZYCAADj2Q42ZWVlOn/+vNatWyePx6Pc3Fw1NDT4T/49e/ZswFdK8+fP186dO7VmzRqtXr1as2bN0t69e/3XsJGkH//4x/J6vXr66ad16dIlPfzww2poaAjJNWzi4uK0fv36aw5dRTrm5VrMSXDMS3DMS3DMS3DMy7XCNSe2r2MDAAAwVoXubB0AAIBRRrABAADGINgAAABjEGwAAIAxjAg2tbW1ysrKUnx8vAoLC3X06NHr1u/evVtz5sxRfHy87rvvPu3fv3+ERjpy7MzJjh07FBUVFfAw8a7qb775pv7sz/5M6enpioqK8t+v7Hqampr0wAMPKC4uTjNnztSOHTvCPs6RZndempqartlfoqKihrwFynjkdruVn5+vyZMnKyUlRaWlpTp16tQN1zP9vWU48xIJ7y8vv/yyvva1r/kvvldUVKT/+q//uu46pu8rkv15CdW+Mu6DTX19vVwul9avX6/W1lbl5OSopKREnZ2dQesPHz6sRYsW6cknn9Tx48dVWlqq0tJSnThxYoRHHj5250T6/GqYn376qf9x5syZERzxyPB6vcrJyVFtbe1N1X/00UdauHChvv71r6utrU1VVVX6y7/8S/3yl78M80hHlt15uerUqVMB+0xKSkqYRjjyDh06pKVLl+rIkSM6cOCABgYG9I1vfENer3fIdSLhvWU48yKZ//4ybdo0bdiwQS0tLTp27Jj++I//WN/61rf03nvvBa2PhH1Fsj8vUoj2FWucKygosJYuXep/Pjg4aKWnp1tutzto/fe//31r4cKFAcsKCwutv/qrvwrrOEeS3Tl55ZVXrKSkpBEa3dggydqzZ891a3784x9b9957b8CysrIyq6SkJIwjG103My+//vWvLUnWxYsXR2RMY0FnZ6clyTp06NCQNZHw3vJlNzMvkfj+YlmWNWXKFOuf//mfg74WifvKVdebl1DtK+P6G5v+/n61tLTI6XT6l0VHR8vpdKq5uTnoOs3NzQH1klRSUjJk/XgznDmRpJ6eHk2fPl2ZmZk3TNSRwvR95Vbl5ubqzjvv1J/+6Z/qN7/5zWgPJ6y6urokSbfffvuQNZG4v9zMvEiR9f4yODioXbt2yev1DnlboEjcV25mXqTQ7CvjOthcuHBBg4OD/qseX5Wamjrk8X6Px2OrfrwZzpzMnj1b27dv1y9+8Qv9/Oc/l8/n0/z58/W///u/IzHkMWuofaW7u1u9vb2jNKrRd+edd6qurk6vvvqqXn31VWVmZmrBggVqbW0d7aGFhc/nU1VVlR566KGAK6Z/menvLV92s/MSKe8v7777riZNmqS4uDg988wz2rNnj+bOnRu0NpL2FTvzEqp9xfYtFWCeoqKigAQ9f/583XPPPfrJT36i5557bhRHhrFo9uzZmj17tv/5/Pnz9cEHH+jFF1/Uz372s1EcWXgsXbpUJ06c0FtvvTXaQxlTbnZeIuX9Zfbs2Wpra1NXV5f+4z/+Q+Xl5Tp06NCQH+KRws68hGpfGdfBZurUqYqJiVFHR0fA8o6ODqWlpQVdJy0tzVb9eDOcOfmy2NhY3X///Tp9+nQ4hjhuDLWvJCYmKiEhYZRGNTYVFBQY+cFfWVmpffv26c0339S0adOuW2v6e8sfsjMvX2bq+4vD4dDMmTMlSXl5eXr77bf10ksv6Sc/+ck1tZG0r9iZly8b7r4yrg9FORwO5eXlqbGx0b/M5/OpsbFxyGN4RUVFAfWSdODAgese8xtPhjMnXzY4OKh3331Xd955Z7iGOS6Yvq+EUltbm1H7i2VZqqys1J49e/TGG29oxowZN1wnEvaX4czLl0XK+4vP51NfX1/Q1yJhXxnK9ebly4a9r9zy6cejbNeuXVZcXJy1Y8cO6+TJk9bTTz9tJScnWx6Px7Isy1q8eLG1atUqf/1vfvMba8KECdbGjRut999/31q/fr0VGxtrvfvuu6O1CSFnd06effZZ65e//KX1wQcfWC0tLdYTTzxhxcfHW++9995obUJYXL582Tp+/Lh1/PhxS5K1efNm6/jx49aZM2csy7KsVatWWYsXL/bXf/jhh9Ztt91mrVy50nr//fet2tpaKyYmxmpoaBitTQgLu/Py4osvWnv37rV+//vfW++++661fPlyKzo62jp48OBobULI/fVf/7WVlJRkNTU1WZ9++qn/ceXKFX9NJL63DGdeIuH9ZdWqVdahQ4esjz76yHrnnXesVatWWVFRUdavfvUry7Iic1+xLPvzEqp9ZdwHG8uyrC1btlh33XWX5XA4rIKCAuvIkSP+14qLi63y8vKA+n//93+3vvrVr1oOh8O69957rddff32ERxx+duakqqrKX5uammo99thjVmtr6yiMOryu/kz5y4+rc1FeXm4VFxdfs05ubq7lcDisu+++23rllVdGfNzhZndenn/+eeuP/uiPrPj4eOv222+3FixYYL3xxhujM/gwCTYfkgL++0fie8tw5iUS3l9++MMfWtOnT7ccDof1la98xfqTP/kT/4e3ZUXmvmJZ9uclVPtKlGVZlr3veAAAAMamcX2ODQAAwB8i2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGP8f0kXtqutxFRYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I skipped this for OH_1850COIsnap15, not working right\")\n",
    "print(\"this MUSTFREE code doesn't work right if a set of border units is surrounded by other border units\")\n",
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "stillCantFill = list()  #those we can't fill due to border topology problem\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarterList = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))\n",
    "        if len(HDstarterList) == 0:  #we have multiple edge enclaves; this is too hard for MUSTFREE\n",
    "            print(t,\"probably has multiple edge enclaves; can't solve in this block\")\n",
    "            stillCantFill.append(t)\n",
    "            enclaveLists = list()\n",
    "            break\n",
    "        HDstarter = [0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            #WARNING: this code probably isn't robust for multiple edge enclaves\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1 and len(HDborderSublists) > j :  #latter evaln is to prevent an index error\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        if len(remainingHDborderSet) > 0: #for narrow peninsulas, separated border units may connect\n",
    "                            #this if statement was added for legisl OH\n",
    "                            HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                            HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a standard unit ##vtd in vanilla code\n",
    "                    c = countyNo[u] #countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if \"weDistinguishTinyCounties\" == \"yup\": #countyPop[c] < 0.1 * aDP: ##plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if len(freeingUnits) <= len(enclaveLists):  #this is new for COI615 / 1850 Congr\n",
    "            print(\"giving up for HD\",t,\"there are only\",len(freeingUnits),\"freeing units for\",len(enclaveLists),\"enclaves\")\n",
    "            stillCantFill.append(t)\n",
    "            break\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]     #update from vanilla HD\n",
    "            starterU = homeU[t] #HDunitList[t][distList.index(np.min(distList))]  #update from vanilla HD\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            if homeU[t] in freeingUnits[dropped_sNo]:\n",
    "                print(\"WARNING, about to drop unit\",homeU[t],\"from HD\",t,\"; its dropped_sNo is\",dropped_sNo)\n",
    "                print(\"  Is the homeU in the final set of freeing units?\",(homeU[t] in freeingUnits[-1]) )\n",
    "                print(\" len of enclave lists and freeing units (prior to drop) is\",len(enclaveLists),len(freeingUnits) )\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "lostHomeUnit = list()\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        lostHomeUnit.append(t)\n",
    "        revPop = r3((HDvPop[t] + unitPop[homeU[t]])/aDP)\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t,\"actual, plus pop/aDP =\",r3(HDvPop[t]/aDP),revPop)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "e31cbd7e-01e5-4ee1-8aba-a01f0505db1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking for big surround for unit 0\n",
      "checking for big surround for unit 500\n",
      "checking for big surround for unit 1000\n",
      "checking for big surround for unit 1500\n",
      "checking for big surround for unit 2000\n",
      "checking for big surround for unit 2500\n",
      "checking for big surround for unit 3000\n"
     ]
    }
   ],
   "source": [
    "print(\"if above had a lot of fails, may have an undetected surround\")\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"checking for big surround for unit\",u)\n",
    "    nbrNbrs = [len(unitNbrs[uu]) for uu in unitNbrs[u]]\n",
    "    bigNbrU = unitNbrs[u][nbrNbrs.index(np.max(nbrNbrs))]  #the surrounder should have more neighbors that surrounded\n",
    "    loopNo, foundSet = 0, set(unitNbrs[u]).difference({bigNbrU})\n",
    "    while len(foundSet) < 20 and loopNo < 7:\n",
    "        loopNo +=1\n",
    "        for uu in foundSet:\n",
    "            foundSet = foundSet.union(set(unitNbrs[uu]).difference({bigNbrU}) )\n",
    "    if len(foundSet) < 20:\n",
    "        print(\"unit\",u,\"has only\",len(foundSet)+1,\"connected neighbors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "2975a9c2-4546-4db6-a10f-b72141f8c143",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 1\n",
      "working on HD 1402 time is now 2\n",
      "working on HD 2102 time is now 3\n",
      "working on HD 2802 time is now 4\n",
      "working on HD 3503 time is now 5\n",
      "working on HD 4204 time is now 6\n",
      "working on HD 4906 time is now 7\n",
      "working on HD 5606 time is now 8\n",
      "working on HD 6306 time is now 9\n",
      "working on HD 7006 time is now 10\n",
      "working on HD 7706 time is now 11\n",
      "working on HD 8407 time is now 12\n",
      "out of 8934 total HDs, there were 8866.0 contiguous and 8861.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 73 and discontig only= 68\n",
      "stillCantFill enclave only, double, discontigonly 73 0 0\n"
     ]
    }
   ],
   "source": [
    "print(\"update contiguity\")\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "for t in lostHomeUnit:\n",
    "    if t in eLgenerator:\n",
    "        print(\"lost-home unit HD\",t,\"is enclave-only\")\n",
    "    if t in doubleTroubleList:\n",
    "        print(\"lost-home unit HD\",t,\"is double-trouble\")\n",
    "    if t in sPgenerator:\n",
    "        print(\"lost-home unit HD\",t,\"is discontig-only\")\n",
    "\n",
    "stillCantFilleLgenerator = set(stillCantFill).intersection(set(eLgenerator))\n",
    "stillCantFilldoubleTroubleList = set(stillCantFill).intersection(set(doubleTroubleList))\n",
    "stillCantFillsPgenerator = set(stillCantFill).intersection(set(sPgenerator))\n",
    "print(\"stillCantFill enclave only, double, discontigonly\",len(stillCantFilleLgenerator),\n",
    "      len(stillCantFilldoubleTroubleList),len(stillCantFillsPgenerator) )"
   ]
  },
  {
   "cell_type": "raw",
   "id": "98c5fe17-7180-4369-917c-1e8f412570d5",
   "metadata": {},
   "source": [
    "print(\"special for COI-congr - force-fill the enclave-only, fix pops later\")\n",
    "for iii, t in enumerate(stillCantFilleLgenerator):\n",
    "    if iii%(20) == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"stubborn enclavy HDs.\" )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if \"force\" == \"force\": #HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0d32a7-0a31-4163-b09a-e6ce4ac9a7cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "d9005d35-ce34-494b-890a-a11843753272",
   "metadata": {},
   "source": [
    "print(\"classify updated unit HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "d5ff711c-4bf6-437e-89fe-f30dda1a51a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xp40bN6q4uFgNDQ0aPXq0jh07ZtXMmjVLb731ll577TWtW7dO+/bt02233WbNb2pqUm5ururr6/Xhhx/qxRdfVFFRkQoLC62ayspK5ebm6oYbblB5eblmzpype++9V6tXc10nAADw3XEYY8y5PvngwYNKTk7WunXrNHLkSNXW1qpnz55aunSpbr/9dknSrl27NHDgQJWWlmrEiBF65513dNNNN2nfvn1KSUmRJC1ZskRz5szRwYMH5XK5NGfOHK1cuVLbt2+3Xmv8+PGqqanRqlWrzqq3YDAor9er2tpaeTyec11FAB3M8frGdr0YLjtzA+HV0b6/z2sfpdraWklSUlKSJKmsrEwNDQ3Kzv7HWXMHDBig3r17q7S0VJJUWlqqwYMHWyFJknJychQMBrVjxw6r5uvLaKlpWcbp1NXVKRgMhkwAAADn45yDUnNzs2bOnKlrrrlGgwYNkiQFAgG5XC4lJiaG1KakpCgQCFg1Xw9JLfNb5n1bTTAY1IkTJ07bz/z58+X1eq0pPT39XFcNQAfWJdapPz14Q7jbABAlzjko5eXlafv27XrllVfasp9zVlBQoNraWmv6/PPPw90SgHYQE+NQelJCuNsAECXO6YSTM2bM0IoVK7R+/Xr16tXLetzn86m+vl41NTUhW5Wqq6vl8/msms2bN4csr+WouK/XfPNIuerqank8HsXHx5+2J7fbLbeb6z8BAIC206otSsYYzZgxQ8uXL9eaNWuUkZERMn/48OGKi4tTSUmJ9VhFRYWqqqrk9/slSX6/X9u2bdOBAwesmuLiYnk8HmVmZlo1X19GS03LMgBEr/rGZv3b21zCBMB3o1VblPLy8rR06VK9+eab6tatm7VPkdfrVXx8vLxeryZPnqz8/HwlJSXJ4/Hovvvuk9/v14gRIyRJo0ePVmZmpu6++24tXLhQgUBADz/8sPLy8qwtQtOmTdPvf/97Pfjgg/rZz36mNWvWaNmyZVq58uwvQwCgc2psbtYf1v8l3G0AiBKt2qK0ePFi1dbW6vrrr1dqaqo1vfrqq1bNU089pZtuuknjxo3TyJEj5fP59Prrr1vznU6nVqxYIafTKb/fr5/85CeaOHGi5s2bZ9VkZGRo5cqVKi4u1tChQ/XEE0/oueeeU05OThusMgAAwNk5r/ModWQd7TwMANoG51ECOreO9v3Ntd4AAABsEJQAAABsEJQAAABsEJQAAABsEJQARJQusU69O2tkuNsAECUISgAiSkyMQ5emdAt3GwCiBEEJAADAxjld6w0AwqW+sVmL3t8d7jYARAm2KAGIKI3NzXq65LNwtwEgShCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAEQUd6xTb+ZdE+42AEQJghKAiOKMcWhoemK42wAQJQhKAAAANriECYCIUt/YrBc2VIa7DQBRgi1KACJKY3Oz5r+zK9xtAIgSBCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAAAAbBCUAEcUd69TLU0aEuw0AUYKgBCCiOGMc8l9yYbjbABAlCEoAAAA2uIQJgIjS0NSslzdXhbsNAFGCLUoAIkpDU7MK39wR7jYARAmCEgAAgA2CEgAAgA2CEgAAgA2CEgAAgA2CEgAAgA2CEgAAgA2CEoCI4nLG6PmfXhnuNgBECYISgIgS64zRDwakhLsNAFGCoAQAAGCDS5gAiCgNTc164+O/hbsNAFGCLUoAIkpDU7Nm/5+t4W4DQJQgKAEAANggKAEAANggKAEAANggKAEAANggKAEAANhodVBav369br75ZqWlpcnhcOiNN94Imf/Tn/5UDocjZBozZkxIzeHDhzVhwgR5PB4lJiZq8uTJOnr0aEjN1q1bdd1116lLly5KT0/XwoULW792AAAA56HVQenYsWMaOnSoFi1aZFszZswY7d+/35pefvnlkPkTJkzQjh07VFxcrBUrVmj9+vWaOnWqNT8YDGr06NHq06ePysrK9Pjjj2vu3Ln6wx/+0Np2AXQyLmeMFt01LNxtAIgSrT7h5NixYzV27NhvrXG73fL5fKed9+mnn2rVqlX66KOPdOWVX12v6ZlnntGNN96o3/zmN0pLS9NLL72k+vp6Pf/883K5XLrssstUXl6uJ598MiRQAYg+sc4Y5Q5JVd7ScHcCIBq0yz5Ka9euVXJysvr376/p06fr0KFD1rzS0lIlJiZaIUmSsrOzFRMTo02bNlk1I0eOlMvlsmpycnJUUVGhL7/88rSvWVdXp2AwGDIBAACcjzYPSmPGjNF//dd/qaSkRP/+7/+udevWaezYsWpqapIkBQIBJScnhzwnNjZWSUlJCgQCVk1KSuhFL1vut9R80/z58+X1eq0pPT29rVcNQAfQ2NSslVv3h7sNAFGiza/1Nn78eOv24MGDNWTIEF1yySVau3atRo0a1dYvZykoKFB+fr51PxgMEpaATqi+qVl5S/8c7jYARIl2Pz1A37591aNHD+3evVuS5PP5dODAgZCaxsZGHT582Nqvyefzqbq6OqSm5b7dvk9ut1sejydkAgAAOB/tHpS++OILHTp0SKmpqZIkv9+vmpoalZWVWTVr1qxRc3OzsrKyrJr169eroaHBqikuLlb//v3VvXv39m4ZAABA0jkEpaNHj6q8vFzl5eWSpMrKSpWXl6uqqkpHjx7V7NmztXHjRu3du1clJSW65ZZb1K9fP+Xk5EiSBg4cqDFjxmjKlCnavHmzNmzYoBkzZmj8+PFKS0uTJN11111yuVyaPHmyduzYoVdffVVPP/10yE9rAAAA7a3VQWnLli264oordMUVV0iS8vPzdcUVV6iwsFBOp1Nbt27Vj370I1166aWaPHmyhg8frj/96U9yu93WMl566SUNGDBAo0aN0o033qhrr7025BxJXq9X7777riorKzV8+HDdf//9Kiws5NQAAADgO+UwxphwN9EegsGgvF6vamtr2V8J6ESO1zcqs3B1uy1/74Lcdls2gDPraN/fXOsNAADABkEJQESJc8bo8duHhLsNAFGCoAQgosQ5Y/Q/ruQcaQC+GwQlAAAAG21+Zm4AaE+NTc1a/9nBcLcBIEqwRQlARKlvatbPiraEuw0AUYKgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBCCixDljNO+Wy8LdBoAoQVACEFHinDGa6L843G0AiBIEJQAAABtcwgRARGlqNtpceTjcbQCIEmxRAhBR6hqbdOf/2hjuNgBECYISAACADYISAACADYISAACADYISAACADYISAACADYISAACADYISgIgSGxOjgrEDwt0GgChBUAIQUVyxMfr59y8JdxsAogRBCQAAwAaXMAEQUZqajbb/rTbcbQCIEmxRAhBR6hqbdMuiDeFuA0CUICgBAADYICgBAADYICgBAADYICgBAADYICgBAADYICgBAADYICgBiCixMTH6l1HfC3cbAKIEQQlARHHFxmjWDy8NdxsAogRBCQAAwAaXMAEQUZqbjXYfPBruNgBECbYoAYgoJxubNPqp9eFuA0CUICgBAADYICgBAADYICgBAADYICgBAADYICgBAADYICgBAADYICgBiCixMTGaOrJvuNsAECUISgAiiis2Rv9648BwtwEgSrQ6KK1fv14333yz0tLS5HA49MYbb4TMN8aosLBQqampio+PV3Z2tj777LOQmsOHD2vChAnyeDxKTEzU5MmTdfRo6Jl2t27dquuuu05dunRRenq6Fi5c2Pq1AwAAOA+tDkrHjh3T0KFDtWjRotPOX7hwoX73u99pyZIl2rRpky644ALl5OTo5MmTVs2ECRO0Y8cOFRcXa8WKFVq/fr2mTp1qzQ8Ggxo9erT69OmjsrIyPf7445o7d67+8Ic/nMMqAuhMmpuNPj98PNxtAIgSDmOMOecnOxxavny5br31VklfbU1KS0vT/fffrwceeECSVFtbq5SUFBUVFWn8+PH69NNPlZmZqY8++khXXnmlJGnVqlW68cYb9cUXXygtLU2LFy/WL3/5SwUCAblcLknSQw89pDfeeEO7du06q96CwaC8Xq9qa2vl8XjOdRUBdDDH6xuVWbi63Za/d0Fuuy0bwJl1tO/vNt1HqbKyUoFAQNnZ2dZjXq9XWVlZKi0tlSSVlpYqMTHRCkmSlJ2drZiYGG3atMmqGTlypBWSJCknJ0cVFRX68ssvT/vadXV1CgaDIRMAAMD5aNOgFAgEJEkpKSkhj6ekpFjzAoGAkpOTQ+bHxsYqKSkppOZ0y/j6a3zT/Pnz5fV6rSk9Pf38VwgAAES1TnPUW0FBgWpra63p888/D3dLAAAgwrVpUPL5fJKk6urqkMerq6uteT6fTwcOHAiZ39jYqMOHD4fUnG4ZX3+Nb3K73fJ4PCETAADA+WjToJSRkSGfz6eSkhLrsWAwqE2bNsnv90uS/H6/ampqVFZWZtWsWbNGzc3NysrKsmrWr1+vhoYGq6a4uFj9+/dX9+7d27JlAAAAW60OSkePHlV5ebnKy8slfbUDd3l5uaqqquRwODRz5kz9+te/1h//+Edt27ZNEydOVFpamnVk3MCBAzVmzBhNmTJFmzdv1oYNGzRjxgyNHz9eaWlpkqS77rpLLpdLkydP1o4dO/Tqq6/q6aefVn5+fputOAAAwJnEtvYJW7Zs0Q033GDdbwkvkyZNUlFRkR588EEdO3ZMU6dOVU1Nja699lqtWrVKXbp0sZ7z0ksvacaMGRo1apRiYmI0btw4/e53v7Pme71evfvuu8rLy9Pw4cPVo0cPFRYWhpxrCUB0csY4dPeIPvrfG/8a7lYARIHzOo9SR9bRzsMAoG1d/NDKdlku51ECwqujfX93mqPeAAAA2lqrf3oDgHAyxujwsfpwtwEgSrBFCUBEOdHQpOG/fi/cbQCIEgQlAAAAGwQlAAAAGwQlAAAAGwQlAAAAGwQlAAAAGwQlAAAAGwQlABHFGePQuGG9wt0GgChBUAIQUdyxTj3x46HhbgNAlCAoAQAA2OASJgAiijFGJxqawt0GgCjBFiUAEeVEQ5MyC1eHuw0AUYKgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBAAAYIOgBCCixDgcunGwL9xtAIgSBCUAEaVLnFPPThge7jYARAmCEgAAgA2CEgAAgA2CEoCIcry+URc/tDLcbQCIEgQlAAAAG7HhbgAAOpLWbq3auyC3nToB0BGwRQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQlARIlxOHRD/57hbgNAlCAoAYgoXeKceuGeq8PdBoAoQVACAACwQVACAACwQVACEFGO1zdq4K9WhbsNAFGCoAQg4pxoaAp3CwCiBEEJAADABkEJAADABkEJAADABkEJAADABkEJAADABkEJQESJcTiUlZEU7jYARAmCEoCI0iXOqVd/7g93GwCiBEEJAADABkEJAADABkEJQEQ5Xt+oYY8Vh7sNAFGizYPS3Llz5XA4QqYBAwZY80+ePKm8vDxdeOGF6tq1q8aNG6fq6uqQZVRVVSk3N1cJCQlKTk7W7Nmz1djY2NatAohQh4/Vh7sFAFEitj0Wetlll+m99977x4vE/uNlZs2apZUrV+q1116T1+vVjBkzdNttt2nDhg2SpKamJuXm5srn8+nDDz/U/v37NXHiRMXFxenf/u3f2qNdAACA02qXoBQbGyufz3fK47W1tfrP//xPLV26VD/4wQ8kSS+88IIGDhyojRs3asSIEXr33Xe1c+dOvffee0pJSdHll1+uxx57THPmzNHcuXPlcrnao2UAAIBTtMs+Sp999pnS0tLUt29fTZgwQVVVVZKksrIyNTQ0KDs726odMGCAevfurdLSUklSaWmpBg8erJSUFKsmJydHwWBQO3bssH3Nuro6BYPBkAkAAOB8tHlQysrKUlFRkVatWqXFixersrJS1113nY4cOaJAICCXy6XExMSQ56SkpCgQCEiSAoFASEhqmd8yz878+fPl9XqtKT09vW1XDAAARJ02/+lt7Nix1u0hQ4YoKytLffr00bJlyxQfH9/WL2cpKChQfn6+dT8YDBKWAADAeWn30wMkJibq0ksv1e7du+Xz+VRfX6+ampqQmurqamufJp/Pd8pRcC33T7ffUwu32y2PxxMyAeh8YhwODenlDXcbAKJEuwelo0ePas+ePUpNTdXw4cMVFxenkpISa35FRYWqqqrk9391SQK/369t27bpwIEDVk1xcbE8Ho8yMzPbu10AHVyXOKf+OOPacLcBIEq0+U9vDzzwgG6++Wb16dNH+/bt0yOPPCKn06k777xTXq9XkydPVn5+vpKSkuTxeHTffffJ7/drxIgRkqTRo0crMzNTd999txYuXKhAIKCHH35YeXl5crvdbd0uAACArTYPSl988YXuvPNOHTp0SD179tS1116rjRs3qmfPnpKkp556SjExMRo3bpzq6uqUk5OjZ5991nq+0+nUihUrNH36dPn9fl1wwQWaNGmS5s2b19atAgAAfCuHMcaEu4n2EAwG5fV6VVtby/5KQCdyor5J2U+u099qToS7FUnS3gW54W4B6FQ62vd3u5xwEgDa0sUPrQx3CwCiFBfFBQAAsEFQAgAAsEFQAgAAsEFQAgAAsEFQAgAAsEFQAgAAsEFQAgAAsEFQAgAAsMEJJwHgPHSEk2FydnCg/bBFCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwAZBCQAAwEZsuBsAOpuLH1rZLsvduyC3XZYLALDXoYPSokWL9PjjjysQCGjo0KF65plndPXVV4e7LQBnqb1CIwB8VzpsUHr11VeVn5+vJUuWKCsrS7/97W+Vk5OjiooKJScnh7s9AOgwIjWQspUUkaDD7qP05JNPasqUKbrnnnuUmZmpJUuWKCEhQc8//3y4WwMAAFGiQ25Rqq+vV1lZmQoKCqzHYmJilJ2drdLS0tM+p66uTnV1ddb92tpaSVIwGGzz/gY9srrNlwmcSe9Zr4W7BaBNtcfnMyJfy78LY0yYO/lKhwxKf//739XU1KSUlJSQx1NSUrRr167TPmf+/Pl69NFHT3k8PT29XXoEAJwf72/D3QE6siNHjsjr9Ya7jY4ZlM5FQUGB8vPzrfvNzc06fPiwLrzwQjkcjjZ7nWAwqPT0dH3++efyeDxttlycGWMfPox9eDDu4cPYh0fLuO/cuVNpaWnhbkdSBw1KPXr0kNPpVHV1dcjj1dXV8vl8p32O2+2W2+0OeSwxMbG9WpTH4+GPJ0wY+/Bh7MODcQ8fxj48LrroIsXEdIzdqDtGF9/gcrk0fPhwlZSUWI81NzerpKREfr8/jJ0BAIBo0iG3KElSfn6+Jk2apCuvvFJXX321fvvb3+rYsWO65557wt0aAACIEh02KN1xxx06ePCgCgsLFQgEdPnll2vVqlWn7OD9XXO73XrkkUdO+ZkP7Y+xDx/GPjwY9/Bh7MOjI467w3SU4+8AAAA6mA65jxIAAEBHQFACAACwQVACAACwQVACAACwQVBqpUWLFuniiy9Wly5dlJWVpc2bN4e7pQ5r7ty5cjgcIdOAAQOs+SdPnlReXp4uvPBCde3aVePGjTvlJKNVVVXKzc1VQkKCkpOTNXv2bDU2NobUrF27VsOGDZPb7Va/fv1UVFR0Si+d/X1bv369br75ZqWlpcnhcOiNN94ImW+MUWFhoVJTUxUfH6/s7Gx99tlnITWHDx/WhAkT5PF4lJiYqMmTJ+vo0aMhNVu3btV1112nLl26KD09XQsXLjyll9dee00DBgxQly5dNHjwYL399tut7iVSnGncf/rTn57yNzBmzJiQGsa99ebPn6+rrrpK3bp1U3Jysm699VZVVFSE1HSkz5ez6SVSnM3YX3/99af8u582bVpITUSNvcFZe+WVV4zL5TLPP/+82bFjh5kyZYpJTEw01dXV4W6tQ3rkkUfMZZddZvbv329NBw8etOZPmzbNpKenm5KSErNlyxYzYsQI80//9E/W/MbGRjNo0CCTnZ1tPv74Y/P222+bHj16mIKCAqvmL3/5i0lISDD5+flm586d5plnnjFOp9OsWrXKqomG9+3tt982v/zlL83rr79uJJnly5eHzF+wYIHxer3mjTfeMJ988on50Y9+ZDIyMsyJEyesmjFjxpihQ4eajRs3mj/96U+mX79+5s4777Tm19bWmpSUFDNhwgSzfft28/LLL5v4+HjzH//xH1bNhg0bjNPpNAsXLjQ7d+40Dz/8sImLizPbtm1rVS+R4kzjPmnSJDNmzJiQv4HDhw+H1DDurZeTk2NeeOEFs337dlNeXm5uvPFG07t3b3P06FGrpiN9vpypl0hyNmP//e9/30yZMiXk331tba01P9LGnqDUCldffbXJy8uz7jc1NZm0tDQzf/78MHbVcT3yyCNm6NChp51XU1Nj4uLizGuvvWY99umnnxpJprS01Bjz1ZdQTEyMCQQCVs3ixYuNx+MxdXV1xhhjHnzwQXPZZZeFLPuOO+4wOTk51v1oe9+++YXd3NxsfD6fefzxx63HampqjNvtNi+//LIxxpidO3caSeajjz6yat555x3jcDjM3/72N2OMMc8++6zp3r27NfbGGDNnzhzTv39/6/6Pf/xjk5ubG9JPVlaW+fnPf37WvUQqu6B0yy232D6HcW8bBw4cMJLMunXrjDEd6/PlbHqJZN8ce2O+Ckr/8i//YvucSBt7fno7S/X19SorK1N2drb1WExMjLKzs1VaWhrGzjq2zz77TGlpaerbt68mTJigqqoqSVJZWZkaGhpCxnPAgAHq3bu3NZ6lpaUaPHhwyElGc3JyFAwGtWPHDqvm68toqWlZBu+bVFlZqUAgEDIGXq9XWVlZIWOdmJioK6+80qrJzs5WTEyMNm3aZNWMHDlSLpfLqsnJyVFFRYW+/PJLq+bb3o+z6aWzWbt2rZKTk9W/f39Nnz5dhw4dsuYx7m2jtrZWkpSUlCSpY32+nE0vkeybY9/ipZdeUo8ePTRo0CAVFBTo+PHj1rxIG/sOe2bujubvf/+7mpqaTjkzeEpKinbt2hWmrjq2rKwsFRUVqX///tq/f78effRRXXfdddq+fbsCgYBcLtcpFy5OSUlRIBCQJAUCgdOOd8u8b6sJBoM6ceKEvvzyy6h/31rG6nRj8PVxTE5ODpkfGxurpKSkkJqMjIxTltEyr3v37rbvx9eXcaZeOpMxY8botttuU0ZGhvbs2aN//dd/1dixY1VaWiqn08m4t4Hm5mbNnDlT11xzjQYNGiRJHerz5Wx6iVSnG3tJuuuuu9SnTx+lpaVp69atmjNnjioqKvT6669LiryxJyih3YwdO9a6PWTIEGVlZalPnz5atmyZ4uPjw9gZ8N0YP368dXvw4MEaMmSILrnkEq1du1ajRo0KY2edR15enrZv364PPvgg3K1EHbuxnzp1qnV78ODBSk1N1ahRo7Rnzx5dcskl33Wb542f3s5Sjx495HQ6T9lbvrq6Wj6fL0xdRZbExERdeuml2r17t3w+n+rr61VTUxNS8/Xx9Pl8px3vlnnfVuPxeBQfH8/7pn+M1beNgc/n04EDB0LmNzY26vDhw23yfnx9/pl66cz69u2rHj16aPfu3ZIY9/M1Y8YMrVixQu+//7569eplPd6RPl/OppdIZDf2p5OVlSVJIf/uI2nsCUpnyeVyafjw4SopKbEea25uVklJifx+fxg7ixxHjx7Vnj17lJqaquHDhysuLi5kPCsqKlRVVWWNp9/v17Zt20K+SIqLi+XxeJSZmWnVfH0ZLTUty+B9kzIyMuTz+ULGIBgMatOmTSFjXVNTo7KyMqtmzZo1am5utj7k/H6/1q9fr4aGBqumuLhY/fv3V/fu3a2ab3s/zqaXzuyLL77QoUOHlJqaKolxP1fGGM2YMUPLly/XmjVrTvlpsiN9vpxNL5HkTGN/OuXl5ZIU8u8+osb+rHf7hnnllVeM2+02RUVFZufOnWbq1KkmMTExZM99/MP9999v1q5dayorK82GDRtMdna26dGjhzlw4IAx5qvDNnv37m3WrFljtmzZYvx+v/H7/dbzWw4hHT16tCkvLzerVq0yPXv2PO0hpLNnzzaffvqpWbRo0WkPIe3s79uRI0fMxx9/bD7++GMjyTz55JPm448/Nn/961+NMV8dGp6YmGjefPNNs3XrVnPLLbec9vQAV1xxhdm0aZP54IMPzPe+972Qw9RrampMSkqKufvuu8327dvNK6+8YhISEk45TD02Ntb85je/MZ9++ql55JFHTnuY+pl6iRTfNu5HjhwxDzzwgCktLTWVlZXmvffeM8OGDTPf+973zMmTJ61lMO6tN336dOP1es3atWtDDkE/fvy4VdORPl/O1EskOdPY796928ybN89s2bLFVFZWmjfffNP07dvXjBw50lpGpI09QamVnnnmGdO7d2/jcrnM1VdfbTZu3BjuljqsO+64w6SmphqXy2Uuuugic8cdd5jdu3db80+cOGF+8YtfmO7du5uEhATzz//8z2b//v0hy9i7d68ZO3asiY+PNz169DD333+/aWhoCKl5//33zeWXX25cLpfp27eveeGFF07ppbO/b++//76RdMo0adIkY8xXh4f/6le/MikpKcbtdptRo0aZioqKkGUcOnTI3HnnnaZr167G4/GYe+65xxw5ciSk5pNPPjHXXnutcbvd5qKLLjILFiw4pZdly5aZSy+91LhcLnPZZZeZlStXhsw/m14ixbeN+/Hjx83o0aNNz549TVxcnOnTp4+ZMmXKKQGdcW+90425pJC//Y70+XI2vUSKM419VVWVGTlypElKSjJut9v069fPzJ49O+Q8SsZE1tg7/v+KAwAA4BvYRwkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMAGQQkAAMDG/wPWUXEj7dP0yQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.4)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c],0.1)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "c2428bb3-f225-4e1f-aacb-abba4cc2cffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before fixin, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.0455 0.49558\n"
     ]
    }
   ],
   "source": [
    "print(\"before fixin, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "a4885d7f-c4f1-42dc-ba08-2457da5e2cbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.012748943848898695 is the median unitPop / aDP\n"
     ]
    }
   ],
   "source": [
    "print(np.median(unitPop)/aDP,\"is the median unitPop / aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "09019bdd-53b7-418d-a65a-c46da2358f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "badDiscoList = sPgenerator + eLgenerator #cantFill.copy() #sPgenerator.copy()  #prep for below\n",
    "CCBgeom = list()  #no CCBgeoms in this ipynb\n",
    "#HDpoly = HDvtdGeom.copy()  #as shortcut, did not generate these shapes for the COI conversion code\n",
    "HDarea = [np.sum([vtdArea[v] for v in HDvtdList[t] ]) for t in range(nHDs) ]\n",
    "HDcircle = [HDCP[t].buffer(0.23456*HDarea[t]**0.5) for t in range(nHDs) ] #approximation\n",
    "HDdiam = [HDarea[h] for h in range(nHDs)]\n",
    "maxGap = max(0.005*aDP,0.9 * np.median(unitPop) ) # = 0.05 * aDP\n",
    "HDnAddedUnits = [list() for h in range(nHDs)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7324851c-c09b-4c7b-b281-d7afbd3fe478",
   "metadata": {},
   "source": [
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs) ] #RESTART\n",
    "HDvPop =     [latestHDpop[t].copy()  for t in range(nHDs) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "35510fd8-cf15-403b-8e12-ccca4d3ad405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 141\n",
      "swell-generating HD 56 our 1 th HD we must regrow out of 141 0 sec elapsed\n",
      "swell-generating HD 770 our 11 th HD we must regrow out of 141 0 sec elapsed\n",
      "swell-generating HD 974 our 21 th HD we must regrow out of 141 0 sec elapsed\n",
      "swell-generating HD 1142 our 31 th HD we must regrow out of 141 0 sec elapsed\n",
      "swell-generating HD 2815 our 41 th HD we must regrow out of 141 1 sec elapsed\n",
      "swell-generating HD 4758 our 51 th HD we must regrow out of 141 2 sec elapsed\n",
      "swell-generating HD 4966 our 61 th HD we must regrow out of 141 3 sec elapsed\n",
      "swell-generating HD 1291 our 71 th HD we must regrow out of 141 4 sec elapsed\n",
      "swell-generating HD 1932 our 81 th HD we must regrow out of 141 4 sec elapsed\n",
      "swell-generating HD 1992 our 91 th HD we must regrow out of 141 5 sec elapsed\n",
      "swell-generating HD 4792 our 101 th HD we must regrow out of 141 6 sec elapsed\n",
      "swell-generating HD 8127 our 111 th HD we must regrow out of 141 6 sec elapsed\n",
      "swell-generating HD 8189 our 121 th HD we must regrow out of 141 7 sec elapsed\n",
      "swell-generating HD 8230 our 131 th HD we must regrow out of 141 7 sec elapsed\n",
      "swell-generating HD 8265 our 141 th HD we must regrow out of 141 8 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block - stolen from vanillaHD-OHredo\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "#avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(wholeSet) )\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    #                                            subbed HDcircle for HDpoly in below\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "1056f787-fcb6-4927-965a-54abfe4b59ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update classification of HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 1\n",
      "working on HD 1402 time is now 1\n",
      "working on HD 2102 time is now 2\n",
      "working on HD 2802 time is now 3\n",
      "working on HD 3503 time is now 4\n",
      "working on HD 4204 time is now 5\n",
      "working on HD 4906 time is now 6\n",
      "working on HD 5606 time is now 7\n",
      "working on HD 6306 time is now 8\n",
      "working on HD 7006 time is now 9\n",
      "working on HD 7706 time is now 10\n",
      "working on HD 8407 time is now 10\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"update classification of HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "85e8f156-d4f9-497d-8589-b71f639f6d71",
   "metadata": {},
   "source": [
    "print(\"we have one stubborn enclave - where?\")\n",
    "for t in eLgenerator:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)\n",
    "    print(t,HDvPop[t],\"homeU =\",homeU[t],unitPop[homeU[t]])\n",
    "    plotPoly(HDCP[t].buffer(0.1))\n",
    "    for u in HDunitList[t]:\n",
    "        plotPoly(unitGeom[u])\n",
    "        if u == homeU[t]:\n",
    "            plotCenter(\"h\"+str(u),unitGeom[u])\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t]:\n",
    "                plotPoly(unitGeom[uu],0.1)\n",
    "                if uu in enclaveList:\n",
    "                    plotCenter(\"e\",unitGeom[uu])\n",
    "                else:\n",
    "                    plotCenter(\"x\",unitGeom[uu])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "6a132d0c-ec17-41cc-badf-7f466d990ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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p2d+y71xjgsGgjh07dtZ5zZkzR16v13mkpqa29dQAAIAl2hwwV199tSorK7VlyxZNnjxZEyZM0O7duy/F3Npk5syZqq+vdx4HDhwI95QAAMAlEt3WJ7hcLvXr10+SlJmZqW3btmnBggW699571djYqLq6upCrMDU1NUpOTpYkJScna+vWrSHHa7lL6dQxX79zqaamRh6PR7GxsWedl9vtltvtbuvpAAAAC13w58A0NzeroaFBmZmZiomJUVlZmbOvqqpK1dXV8vl8kiSfz6cdO3aotrbWGVNaWiqPx6OMjAxnzKnHaBnTcgwAAIA2XYGZOXOmRo0apbS0NB0+fFjLli3T+vXrVVJSIq/Xq4kTJ6qgoEDdu3eXx+PRI488Ip/Pp+HDh0uSRo4cqYyMDI0fP17z5s1TIBDQrFmzlJeX51w9mTRpkp5//nlNmzZNDz74oNatW6cVK1aouNi+Ow4AAMCl0aaAqa2t1f33368vvvhCXq9X1157rUpKSvTP//zPkqT58+crMjJSubm5amhokN/v16JFi5znR0VFac2aNZo8ebJ8Pp86d+6sCRMm6IknnnDGpKenq7i4WPn5+VqwYIF69+6tF198UX6//yKdMgCEB7d+AxfPBX8OTHvF58AAHZuNMWAjAgbftkv+OTAAAADhQsAAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALBOmwJmzpw5uv7669W1a1clJibqhz/8oaqqqkLGHD9+XHl5eUpISFCXLl2Um5urmpqakDHV1dXKyclRXFycEhMTVVhYqBMnToSMWb9+vYYOHSq3261+/fqpqKjo/M4QAAB0OG0KmA0bNigvL0+bN29WaWmpmpqaNHLkSB09etQZk5+fr9WrV2vlypXasGGDDh48qNGjRzv7T548qZycHDU2NmrTpk1aunSpioqKNHv2bGfM/v37lZOToxEjRqiyslJTp07VQw89pJKSkotwygAAwHYRxhhzvk/+8ssvlZiYqA0bNujmm29WfX29evbsqWXLlunuu++WJO3du1cDBgxQeXm5hg8frrfeeku33367Dh48qKSkJEnSkiVLNH36dH355ZdyuVyaPn26iouLtXPnTue1xowZo7q6Oq1du7ZVcwsGg/J6vaqvr5fH4znfUwTQTvWdURzuKVwWPp2bE+4p4DLT2p/fF/QemPr6eklS9+7dJUkVFRVqampSdna2M6Z///5KS0tTeXm5JKm8vFyDBg1y4kWS/H6/gsGgdu3a5Yw59RgtY1qOcSYNDQ0KBoMhDwAA0DGdd8A0Nzdr6tSp+t73vqdrrrlGkhQIBORyuRQfHx8yNikpSYFAwBlzary07G/Zd64xwWBQx44dO+N85syZI6/X6zxSU1PP99QAAEA7d94Bk5eXp507d2r58uUXcz7nbebMmaqvr3ceBw4cCPeUAADAJRJ9Pk+aMmWK1qxZo40bN6p3797O9uTkZDU2Nqquri7kKkxNTY2Sk5OdMVu3bg05XstdSqeO+fqdSzU1NfJ4PIqNjT3jnNxut9xu9/mcDgAAsEybrsAYYzRlyhS9/vrrWrdundLT00P2Z2ZmKiYmRmVlZc62qqoqVVdXy+fzSZJ8Pp927Nih2tpaZ0xpaak8Ho8yMjKcMaceo2VMyzEAAMDlrU1XYPLy8rRs2TK98cYb6tq1q/OeFa/Xq9jYWHm9Xk2cOFEFBQXq3r27PB6PHnnkEfl8Pg0fPlySNHLkSGVkZGj8+PGaN2+eAoGAZs2apby8POcKyqRJk/T8889r2rRpevDBB7Vu3TqtWLFCxcXcdQAAANp4BWbx4sWqr6/XP/3TP6lXr17O49VXX3XGzJ8/X7fffrtyc3N18803Kzk5Wa+99pqzPyoqSmvWrFFUVJR8Pp/uu+8+3X///XriiSecMenp6SouLlZpaakGDx6sZ555Ri+++KL8fv9FOGUAAGC7C/ocmPaMz4EBOjY+B+bbwefA4Nv2rXwODAAAQDgQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArNPmgNm4caN+8IMfKCUlRREREVq1alXIfmOMZs+erV69eik2NlbZ2dnat29fyJhDhw5p3Lhx8ng8io+P18SJE3XkyJGQMR999JFuuukmderUSampqZo3b17bzw4AAHRIbQ6Yo0ePavDgwVq4cOEZ98+bN0/PPfeclixZoi1btqhz587y+/06fvy4M2bcuHHatWuXSktLtWbNGm3cuFEPP/ywsz8YDGrkyJHq06ePKioq9PTTT+vxxx/XCy+8cB6nCAAAOpoIY4w57ydHROj111/XD3/4Q0lfXX1JSUnRo48+qp/+9KeSpPr6eiUlJamoqEhjxozRnj17lJGRoW3btmnYsGGSpLVr1+r73/++Pv/8c6WkpGjx4sV67LHHFAgE5HK5JEkzZszQqlWrtHfv3lbNLRgMyuv1qr6+Xh6P53xPEUA71XdGcbincFn4dG5OuKeAy0xrf35f1PfA7N+/X4FAQNnZ2c42r9errKwslZeXS5LKy8sVHx/vxIskZWdnKzIyUlu2bHHG3HzzzU68SJLf71dVVZX+9re/nfG1GxoaFAwGQx4AAKBjuqgBEwgEJElJSUkh25OSkpx9gUBAiYmJIfujo6PVvXv3kDFnOsapr/F1c+bMkdfrdR6pqakXfkIAAKBd6jB3Ic2cOVP19fXO48CBA+GeEgAAuEQuasAkJydLkmpqakK219TUOPuSk5NVW1sbsv/EiRM6dOhQyJgzHePU1/g6t9stj8cT8gAAAB3TRQ2Y9PR0JScnq6yszNkWDAa1ZcsW+Xw+SZLP51NdXZ0qKiqcMevWrVNzc7OysrKcMRs3blRTU5MzprS0VFdffbW6det2MacMAAAs1OaAOXLkiCorK1VZWSnpqzfuVlZWqrq6WhEREZo6dar+8z//U7///e+1Y8cO3X///UpJSXHuVBowYIBuu+02/ehHP9LWrVv1xz/+UVOmTNGYMWOUkpIiSfrXf/1XuVwuTZw4Ubt27dKrr76qBQsWqKCg4KKdOAAAsFd0W5+wfft2jRgxwvm6JSomTJigoqIiTZs2TUePHtXDDz+suro63XjjjVq7dq06derkPOeVV17RlClTdOuttyoyMlK5ubl67rnnnP1er1dvv/228vLylJmZqR49emj27NkhnxUDAAAuXxf0OTDtGZ8DA3RsfA7Mt4PPgcG3LSyfAwMAAPBtIGAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFiHgAEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYJzrcEwAAtF99ZxSHewpt9uncnHBPAd8CrsAAAADrEDAAAMA6BAwAALAOAQMAAKxDwAAAAOsQMAAAwDoEDAAAsA4BAwAArEPAAAAA6xAwAADAOgQMAACwDgEDAACsQ8AAAADrEDAAAMA6BAwAALBOuw6YhQsXqm/fvurUqZOysrK0devWcE8JAAC0A9HhnsDZvPrqqyooKNCSJUuUlZWlZ599Vn6/X1VVVUpMTAz39AAA7VTfGcXhnkKbfTo3J9xTsE6EMcaEexJnkpWVpeuvv17PP/+8JKm5uVmpqal65JFHNGPGjG98fjAYlNfrVX19vTwez6WeLmA1G7/hAx0JAfP/tfbnd7u8AtPY2KiKigrNnDnT2RYZGans7GyVl5ef8TkNDQ1qaGhwvq6vr5f01ULAXtf8vCTcU2iznb/wh3sKbdbc8PdwTwG4rPGz6v9rWYtvur7SLgPmL3/5i06ePKmkpKSQ7UlJSdq7d+8ZnzNnzhz94he/OG17amrqJZkjcDbeZ8M9AwC24fvG6Q4fPiyv13vW/e0yYM7HzJkzVVBQ4Hzd3NysQ4cOKSEhQREREWGc2cUTDAaVmpqqAwcO8Guxc2CdWod1ah3WqXVYp9Zhnb6ZMUaHDx9WSkrKOce1y4Dp0aOHoqKiVFNTE7K9pqZGycnJZ3yO2+2W2+0O2RYfH3+pphhWHo+H//BbgXVqHdapdVin1mGdWod1OrdzXXlp0S5vo3a5XMrMzFRZWZmzrbm5WWVlZfL5fGGcGQAAaA/a5RUYSSooKNCECRM0bNgw3XDDDXr22Wd19OhRPfDAA+GeGgAACLN2GzD33nuvvvzyS82ePVuBQEBDhgzR2rVrT3tj7+XE7Xbr5z//+Wm/KkMo1ql1WKfWYZ1ah3VqHdbp4mm3nwMDAABwNu3yPTAAAADnQsAAAADrEDAAAMA6BAwAALAOAXOJ/fnPf9Z9992nhIQExcbGatCgQdq+fbuz3xij2bNnq1evXoqNjVV2drb27dsXcoxDhw5p3Lhx8ng8io+P18SJE3XkyJGQMR999JFuuukmderUSampqZo3b95pc1m5cqX69++vTp06adCgQXrzzTcvzUm30cmTJ/Wzn/1M6enpio2N1T/8wz/oySefDPl3MC7Hddq4caN+8IMfKCUlRREREVq1alXI/va0Jq2Zy6VyrnVqamrS9OnTNWjQIHXu3FkpKSm6//77dfDgwZBjXO7r9HWTJk1SRESEnn322ZDtrNNX9uzZozvuuENer1edO3fW9ddfr+rqamf/8ePHlZeXp4SEBHXp0kW5ubmnfTBrdXW1cnJyFBcXp8TERBUWFurEiRMhY9avX6+hQ4fK7XarX79+KioqOm0uCxcuVN++fdWpUydlZWVp69atF2UdrGBwyRw6dMj06dPH/Nu//ZvZsmWL+eSTT0xJSYn5+OOPnTFz5841Xq/XrFq1ynz44YfmjjvuMOnp6ebYsWPOmNtuu80MHjzYbN682bz77rumX79+ZuzYsc7++vp6k5SUZMaNG2d27txpfve735nY2Fjz61//2hnzxz/+0URFRZl58+aZ3bt3m1mzZpmYmBizY8eOb2cxzuGpp54yCQkJZs2aNWb//v1m5cqVpkuXLmbBggXOmMtxnd58803z2GOPmddee81IMq+//nrI/va0Jq2Zy6VyrnWqq6sz2dnZ5tVXXzV79+415eXl5oYbbjCZmZkhx7jc1+lUr732mhk8eLBJSUkx8+fPD9nHOhnz8ccfm+7du5vCwkLz/vvvm48//ti88cYbpqamxhkzadIkk5qaasrKysz27dvN8OHDzXe/+11n/4kTJ8w111xjsrOzzQcffGDefPNN06NHDzNz5kxnzCeffGLi4uJMQUGB2b17t/nVr35loqKizNq1a50xy5cvNy6Xy/zv//6v2bVrl/nRj35k4uPjQ+bSkREwl9D06dPNjTfeeNb9zc3NJjk52Tz99NPOtrq6OuN2u83vfvc7Y4wxu3fvNpLMtm3bnDFvvfWWiYiIMH/+85+NMcYsWrTIdOvWzTQ0NIS89tVXX+18fc8995icnJyQ18/KyjL//u//fmEneRHk5OSYBx98MGTb6NGjzbhx44wxrJMx5rRvpO1pTVozl2/LuX4wt9i6dauRZD777DNjDOt0qs8//9xcccUVZufOnaZPnz4hAcM6feXee+81991331mfU1dXZ2JiYszKlSudbXv27DGSTHl5uTHmq0iKjIw0gUDAGbN48WLj8XictZs2bZoZOHDgaa/t9/udr2+44QaTl5fnfH3y5EmTkpJi5syZ0/aTtRC/QrqEfv/732vYsGH6l3/5FyUmJuq6667Tb37zG2f//v37FQgElJ2d7Wzzer3KyspSeXm5JKm8vFzx8fEaNmyYMyY7O1uRkZHasmWLM+bmm2+Wy+Vyxvj9flVVVelvf/ubM+bU12kZ0/I64fTd735XZWVl+tOf/iRJ+vDDD/Xee+9p1KhRklinM2lPa9KaubQn9fX1ioiIcP6tNNbpK83NzRo/frwKCws1cODA0/azTl+tUXFxsa666ir5/X4lJiYqKysr5NdMFRUVampqCpl///79lZaWFvJ3c9CgQSEfzOr3+xUMBrVr1y5nzLnWqbGxURUVFSFjIiMjlZ2dHfZ1+rYQMJfQJ598osWLF+s73/mOSkpKNHnyZP34xz/W0qVLJUmBQECSTvt04aSkJGdfIBBQYmJiyP7o6Gh17949ZMyZjnHqa5xtTMv+cJoxY4bGjBmj/v37KyYmRtddd52mTp2qcePGSWKdzqQ9rUlr5tJeHD9+XNOnT9fYsWOdf0iPdfrKf//3fys6Olo//vGPz7ifdZJqa2t15MgRzZ07V7fddpvefvtt3XXXXRo9erQ2bNgg6av5u1yu0/4x4a+f4/muUzAY1LFjx/SXv/xFJ0+ebJfr9G1pt/+UQEfQ3NysYcOG6b/+678kSdddd5127typJUuWaMKECWGeXfuxYsUKvfLKK1q2bJkGDhyoyspKTZ06VSkpKawTLpqmpibdc889MsZo8eLF4Z5Ou1JRUaEFCxbo/fffV0RERLin0241NzdLku68807l5+dLkoYMGaJNmzZpyZIl+sd//MdwTu+ywxWYS6hXr17KyMgI2TZgwADn3erJycmSdNq702tqapx9ycnJqq2tDdl/4sQJHTp0KGTMmY5x6mucbUzL/nAqLCx0rsIMGjRI48ePV35+vubMmSOJdTqT9rQmrZlLuLXEy2effabS0lLn6ovEOknSu+++q9raWqWlpSk6OlrR0dH67LPP9Oijj6pv376SWCdJ6tGjh6Kjo7/x+3pjY6Pq6upCxnz9HM93nTwej2JjY9WjRw9FRUW1y3X6thAwl9D3vvc9VVVVhWz705/+pD59+kiS0tPTlZycrLKyMmd/MBjUli1b5PP5JEk+n091dXWqqKhwxqxbt07Nzc3KyspyxmzcuFFNTU3OmNLSUl199dXq1q2bM+bU12kZ0/I64fT3v/9dkZGh/ylGRUU5/7fDOp2uPa1Ja+YSTi3xsm/fPv3hD39QQkJCyH7WSRo/frw++ugjVVZWOo+UlBQVFhaqpKREEuskSS6XS9dff/05v69nZmYqJiYmZP5VVVWqrq4O+bu5Y8eOkCBsCeuWOPqmdXK5XMrMzAwZ09zcrLKysrCv07cm3O8i7si2bt1qoqOjzVNPPWX27dtnXnnlFRMXF2d++9vfOmPmzp1r4uPjzRtvvGE++ugjc+edd57xVtjrrrvObNmyxbz33nvmO9/5Tsiti3V1dSYpKcmMHz/e7Ny50yxfvtzExcWddutidHS0+Z//+R+zZ88e8/Of/7zd3EY9YcIEc8UVVzi3Ub/22mumR48eZtq0ac6Yy3GdDh8+bD744APzwQcfGEnml7/8pfnggw+cu2fa05q0Zi7hWKfGxkZzxx13mN69e5vKykrzxRdfOI9T75S53NfpTL5+F5IxrJMxX91mHhMTY1544QWzb98+5/bmd9991znGpEmTTFpamlm3bp3Zvn278fl8xufzOftbbqMeOXKkqaysNGvXrjU9e/Y8423UhYWFZs+ePWbhwoVnvI3a7XaboqIis3v3bvPwww+b+Pj4kLubOjIC5hJbvXq1ueaaa4zb7Tb9+/c3L7zwQsj+5uZm87Of/cwkJSUZt9ttbr31VlNVVRUy5q9//asZO3as6dKli/F4POaBBx4whw8fDhnz4YcfmhtvvNG43W5zxRVXmLlz5542lxUrVpirrrrKuFwuM3DgQFNcXHzxT/g8BINB85Of/MSkpaWZTp06mSuvvNI89thjIT9gLsd1euedd4yk0x4TJkwwxrSvNWnNXC6Vc63T/v37z7hPknnnnXecY1zu63QmZwoY1ukrL730kunXr5/p1KmTGTx4sFm1alXIMY4dO2b+4z/+w3Tr1s3ExcWZu+66y3zxxRchYz799FMzatQoExsba3r06GEeffRR09TUdNpchgwZYlwul7nyyivNyy+/fNp8f/WrX5m0tDTjcrnMDTfcYDZv3nzR1qK9izDmlI87BQAAsADvgQEAANYhYAAAgHUIGAAAYB0CBgAAWIeAAQAA1iFgAACAdQgYAABgHQIGAABYh4ABAADWIWAAAIB1CBgAAGAdAgYAAFjn/wEFQnZhiHjk3wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 0.6*aDP:\n",
    "        plotPoly(HDCP[t].buffer(0.01))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "3f2caed4-154e-49e2-a2ed-772b61c9e19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nonsquaredHDlist = [HDunitList[t].copy() for t in range(nHDs)]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "446108ac-7ede-4598-acc0-eed2b84e5435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save these\n",
      "intermediate save to file -- still to address under-overpops\n"
     ]
    }
   ],
   "source": [
    "print(\"save these\")\n",
    "print(\"intermediate save to file -- still to address under-overpops\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC} )\n",
    "outname = STATE+str(int(nHDs))+\"_99COI1850notyetGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ce8b95ef-b68a-4fff-9cc1-2362f217b315",
   "metadata": {},
   "source": [
    "HDunitList = [nonsquaredHDlist[t].copy() for t in range(nHDs)] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "16247f7a-9b5e-462a-bb97-3d75b17ff542",
   "metadata": {},
   "outputs": [],
   "source": [
    "minDistrictPop, maxDistrictPop = 0.95 * aDP, 1.05 * aDP  #legislative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "6b3dd3ff-706e-4e0d-a705-3a82cd62b519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 68 HDs with pop < 113227 to within 5959\n",
      "working on squaring up HD 55 time is now 0\n",
      "We have attempted to address underpop in a total of 68 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList, stillUnderPoppedList = list(),list()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "maxGap = max(maxDistrictPop - aDP,0.9*np.median(unitPop) ) #0.9 * np.median(unitPop)  #maxDistrictPop - aDP \n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnderPoppedList.append(t)\n",
    "print(\"We have attempted to address underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "if len(stillUnderPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillUnderPoppedList))\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a4ee64d2-9c45-468a-b04a-f519ebec5682",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in stillUnderPoppedList:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)\n",
    "    print(t,unbroken,noEnclave)\n",
    "    "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3f4ef69c-68ab-44b3-97fc-f7a2e1276347",
   "metadata": {},
   "source": [
    "print(\"these are stuck because unit\",bigU,\"has multiple internal enclaves.  Fill them\")\n",
    "for u in stillUnderPoppedList:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)\n",
    "    while not noEnclave:\n",
    "        HDunitList[t] += enclaveList\n",
    "        HDvPop[t] += np.sum([unitPop[u] for u in enclaveList])\n",
    "        print(t,\"adding enclaveList\",enclaveList,\"HDvPop now\",int(HDvPop[t]))\n",
    "        unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "28c20a3e-7da5-4c88-9906-8188d9bb481e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update classification of HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 1\n",
      "working on HD 1402 time is now 1\n",
      "working on HD 2102 time is now 2\n",
      "working on HD 2802 time is now 3\n",
      "working on HD 3503 time is now 4\n",
      "working on HD 4204 time is now 5\n",
      "working on HD 4906 time is now 6\n",
      "working on HD 5606 time is now 6\n",
      "working on HD 6306 time is now 7\n",
      "working on HD 7006 time is now 9\n",
      "working on HD 7706 time is now 9\n",
      "working on HD 8407 time is now 10\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"update classification of HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "72398364-3e4a-4a03-a2cf-58a21bdea412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 1.0455 0.49558\n",
      "amped unit avg and sd usage are 1.04774 0.48536\n"
     ]
    },
    {
     "data": {
      "image/png": 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cOBBr1qwBAAwZMgT5+fl4+eWX2/NwtBsGJiIiok7s/PnzWL58OY4ePYqysjI0NTUBAIqKiqQ5DkeNGiXVt9xRPnLkSJt1N86RCACjR4+Gn5+f9NpoNKKqqgrFxcWoqqpCTU0NHnroIZv31NfX46677gIAnDt3DnFxcTbjnXkuRAYmIiKiTuwXv/gFBgwYgDfffBPh4eFoamrCiBEjUF//nxtrVCqV9M8KhcLuupag5YqqqioAQFZWFiIiImzGuuoTMxiYiIiIOqmrV6+ioKAAb775Jh544AEAwOeff+6Wbf/zn/9EbW0tfH2bryP94osvEBAQAIPBgODgYGg0GhQVFWHcuHF233/nnXfib3/7m826L774wi29dQQGJiIiok4qKCgIvXr1whtvvIG+ffuiqKgIzz77rFu2XV9fj9mzZ2PZsmX45ptvsGLFCiQnJ8PHxwc9e/bEM888gwULFqCpqQn3338/KioqcPjwYeh0OiQmJmLevHlYs2YNFi1ahDlz5iA3N7fVdVKdCQMTERGRnLKvvXIfPj4+2LJlC55++mmMGDECQ4YMwauvvorx48ffdjsPPvggBg0ahB//+MewWq2YNm0aXnjhBWn8xRdfRJ8+fZCeno5//etf0Ov1GDNmDJ577jkAQP/+/fHRRx9hwYIFeO211xAbG4uXXnrJ7pNCOgMGJiIiIkf8ejXPvv3xXM/sT+XXvM82iI+Px9mzZ23W3fgQj5sf6DF+/PhW6x5//HE8/vjjrba9cuVKrFy50u5+FQoFUlJSkJKS4rC3n//85/j5z39us67lua+dDQMTERGRI3pD86NK+Cy5bo+BiYiIyBm9gSGGGJiIiIjIVme+OLu9+HR0A0RERETejoGJiIiISAYDExER0b/dfPcYeS9P/7tiYCIiom6v5TEhNTU1HdwJuarl0S9KpdIj++NF30RE1O0plUro9XrpAbR+fn7SM9fI+zQ1NeHKlSvw8/NDjx6eiTIMTNQhwlEGbVk+oAiwO64tq0I4yjzclbzCK1WoExUOx4P81YjQ+3qwIyJyl7CwMACQQhN5Nx8fH/Tv399jwZaBiTxOVXUJ+zSL4Lfd6rAmCsA+jQbFVXcDCPRYb46UVlkRAiBlSx7OOAlMviol9i0cx9BE1AkpFAr07dsXISEhaGho6Oh2SIZarYaPj+euLGJgIo9T1l2Dn8KK4gkZMAyKtltTfD4Phs9SoKy75tnmHKisbUAIgGcmDkGfwbF2awpLq5C6NQ/l1fUMTESdmFKp9Nh1MdR5MDBRh7Hqo4DwaPtjV6o824yLDMG+iIro+DNeRETkWQxM5NWKr9Wi7hKvGSIioo7FwEReSefbfIvvjr37UfhpgcO62h56/HnhFIYmIiJqVwxM5JVCQsLR1MMXGVjvtK5GaFBcMgbQD/NQZ0RE1B0xMJF30hvgk3wcqLnqsMTbLgwnIqKui4GJvJfe0Lw44K0XhhMRUdfDR6MQERERyeAZJqLOylLs9CtLAIBfL6dn6YiIyDUMTESdkaUYyIwFGmQeFKryA5KOMTQREd0mBiaizqjmanNYmvwm0Huw/Zqyr4GP5zbXMjAREd0WBiaif3P2YN0r12oR5eF+XNJ7sMPZ0omIyH0YmKjTu93ZwF15sO5wxUVM0PxnQk0iIupeGJio02oJL7//tABn9tQ7rPNVKbFv4TiHocmVB+tqywKB7UBIgOa2+yYios6HgYk6rZbwkvFoNOp6j7RbU1hahdSteSivrpd9fIrTB+sqAm6rVyIi6twYmKjTi1JcdhhotD5VCEeZhzsiIqKuhoGJOi+/Xs23zX8812FJFIB9Gg2Kq+4G4ODsERERkQwGJuq89IbmOYb4vDkiImpnbX40yqVLl/CrX/0KvXr1gq+vL0aOHIkTJ05I40IILF++HH379oWvry/i4+Nx/vx5m21cu3YN06dPh06ng16vx+zZs1FVZftcsC+//BIPPPAAtFotDAYDVq9e3aqXbdu2YejQodBqtRg5ciR27dplM+5KL9TJ6Q3Nt9U7WKx6r5wMgIiIOpk2Baby8nLcd999UKlU+OSTT3D27FmsWbMGQUFBUs3q1avx6quvYuPGjTh69Cj8/f1hMplQV1cn1UyfPh1nzpzB3r17sXPnThw6dAhPPvmkNF5ZWYmJEydiwIAByM3NxSuvvIIXXngBb7zxhlRz5MgRTJs2DbNnz8apU6cwadIkTJo0CadPn25TL0RERESyRBssWbJE3H///Q7Hm5qaRFhYmHjllVekdRaLRWg0GvHBBx8IIYQ4e/asACCOHz8u1XzyySdCoVCIS5cuCSGEWL9+vQgKChJWq9Vm30OGDJFeP/LIIyIhIcFm/3FxceLXv/61y73IqaioEABERUWFS/XkmvN5/xBiha75pxfsy6V+Lp0SYoWu+acD+d9ZxIAlO0X+d5Zbb9hVLvTjUg0RURfUHn9/t+kM09/+9jeMHTsWDz/8MEJCQnDXXXfhzTfflMYvXrwIs9mM+Ph4aV1gYCDi4uKQk5MDAMjJyYFer8fYsWOlmvj4ePj4+ODo0aNSzY9//GOo1WqpxmQyoaCgAOXl5VLNjftpqWnZjyu93MxqtaKystJmoa5BYykELufZXTSWwg7tjYiIvF+bLvr+17/+hQ0bNiAtLQ3PPfccjh8/jqeffhpqtRqJiYkwm80AgNDQUJv3hYaGSmNmsxkhISG2TfTogeDgYJuayMjIVttoGQsKCoLZbJbdj1wvN0tPT8fKlStdOxjUKTRqg1EjNDB8lgJ8Zr/GAKBGaNCoDfZob0RE1Hm0KTA1NTVh7NixeOmllwAAd911F06fPo2NGzciMTGxXRr0pKVLlyItLU16XVlZCYOBDy3tzBoCIhBvfQV/mjYQUX3sz9VUeKUKMz+4gDcCIjzcHRERdRZtCkx9+/bFsGHDbNbdeeed+OijjwAAYWFhAICSkhL07dtXqikpKUF0dLRUU1paarON69ev49q1a9L7w8LCUFJSYlPT8lqu5sZxuV5uptFooNHw0RddzWX0bp4JPNz+PEx1ogKX4fhZdDbKvnY4pC3jJJlERF1VmwLTfffdh4KCApt1X3/9NQYMGAAAiIyMRFhYGLKzs6VQUllZiaNHj2L+/PkAAKPRCIvFgtzcXMTExAAA9u/fj6amJsTFxUk1v/nNb9DQ0ACVqvl5YXv37sWQIUOkO/KMRiOys7ORmpoq9bJ3714YjUaXeyFyWRsmyfzHN8MADHZYJ/cwYCIi8j5tCkwLFizAvffei5deegmPPPIIjh07hjfeeEO63V+hUCA1NRW//e1vMWjQIERGRuL5559HeHg4Jk2aBKD5jNRPf/pTzJ07Fxs3bkRDQwOSk5Px6KOPIjw8HADw2GOPYeXKlZg9ezaWLFmC06dPIyMjA2vXrpV6SUlJwbhx47BmzRokJCRgy5YtOHHiRJt6IXKZC5NkXis6jeDdSXh151GcEaUO6+QeBtwWhVeqUCfsnx3TllWBs1AREblHmwLT3Xffje3bt2Pp0qVYtWoVIiMjsW7dOkyfPl2qWbx4Maqrq/Hkk0/CYrHg/vvvx+7du6HVaqWa999/H8nJyXjwwQfh4+ODKVOm4NVXX5XGAwMD8emnnyIpKQkxMTHo3bs3li9fbjNX07333ovNmzdj2bJleO655zBo0CDs2LEDI0aMaFMvRC7TG5oXB1ouGXfXw4CdKa2yIgRAypY8nHEQmIYrLiJL859aIiK6dW1+NMrPf/5z/PznP3c4rlAosGrVKqxatcphTXBwMDZv3ux0P6NGjcI//vEPpzUPP/wwHn744dvqhcjdovoEOLxeyl0qaxsQAuCZiUPQZ3Cs3ZorX6uBg/+pJSKiW8dnyRF1YoZgX0RF2A9nhWW8ToqIyF3a/Cw5IiIiou6GZ5iI3I1TDxARdTkMTETu0oapB4qr7gbQvtc5ERGR+zAwEbmLC1MPFJ/Pg+GzFCjrrnmwMSIiul0MTETuJDP1gPVKlQebISIid+FF30REREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIN3yRF5I0ux0+kJNJZCDzZDREQMTETexlIMZMYCDTUOSwwAaoQGjdpgz/VFRNSNMTAReZuaq81hafKbQO/BdksKr1Rh5gcX8EZAhIebIyLqnhiYiLxV78FAeLTdoTpRgcuo8Gw/RETdGAMTkZcqvFKFOmE/FBWWcsZwIiJPYmAi8jKlVVaEAEjZkoczDgITAPiqlAjyV3uuMSKiboyBicjLVNY2IATAMxOHoM/gWId1Qf5qROh9PdcYEVE3xsBE3YKzr7C89estQ7AvoiICO7oNIiICAxN1cUH+aviqlEjdmue0jl9vERGRMwxM1KVF6H2xb+E4lFfXO63j11tEROQMAxN1eRF6X4YhIiK6LXyWHBEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISEabAtMLL7wAhUJhswwdOlQar6urQ1JSEnr16oWAgABMmTIFJSUlNtsoKipCQkIC/Pz8EBISgkWLFuH69es2NQcOHMCYMWOg0WgQFRWFTZs2teolMzMTd9xxB7RaLeLi4nDs2DGbcVd6ISIiInJFm88wDR8+HN9//720fP7559LYggUL8Pe//x3btm3DwYMHcfnyZUyePFkab2xsREJCAurr63HkyBG899572LRpE5YvXy7VXLx4EQkJCZgwYQLy8vKQmpqKOXPmYM+ePVLN1q1bkZaWhhUrVuDkyZMYPXo0TCYTSktLXe6Fbs0lSy1OX6pwulyy1HZ0m0RERG7Vo81v6NEDYWFhrdZXVFTg7bffxubNm/GTn/wEAPDuu+/izjvvxBdffIF77rkHn376Kc6ePYt9+/YhNDQU0dHRePHFF7FkyRK88MILUKvV2LhxIyIjI7FmzRoAwJ133onPP/8ca9euhclkAgD84Q9/wNy5c/HEE08AADZu3IisrCy88847ePbZZ13qhdrukqUW8WsOorah0Wmdr0qJfQvHIULv66HOiIiI2lebA9P58+cRHh4OrVYLo9GI9PR09O/fH7m5uWhoaEB8fLxUO3ToUPTv3x85OTm45557kJOTg5EjRyI0NFSqMZlMmD9/Ps6cOYO77roLOTk5NttoqUlNTQUA1NfXIzc3F0uXLpXGfXx8EB8fj5ycHABwqRd7rFYrrFar9LqysrKth6dLK6+uR21DI9ZNjUZUSIDdmsLSKqRuzUN5dT0DExERdRltCkxxcXHYtGkThgwZgu+//x4rV67EAw88gNOnT8NsNkOtVkOv19u8JzQ0FGazGQBgNpttwlLLeMuYs5rKykrU1taivLwcjY2Ndmu++uoraRtyvdiTnp6OlStXunYwurGokACMiAjs6DaIiIg8pk2B6Wc/+5n0z6NGjUJcXBwGDBiADz/8EL6+nf9swtKlS5GWlia9rqyshMFg6MCOiIiIyBvc1rQCer0egwcPRmFhIcLCwlBfXw+LxWJTU1JSIl3zFBYW1upOtZbXcjU6nQ6+vr7o3bs3lEql3ZobtyHXiz0ajQY6nc5mISIiIrqtwFRVVYULFy6gb9++iImJgUqlQnZ2tjReUFCAoqIiGI1GAIDRaER+fr7N3Wx79+6FTqfDsGHDpJobt9FS07INtVqNmJgYm5qmpiZkZ2dLNa70Qu2rsLTK4V10xdd4Fx0REXUubfpK7plnnsEvfvELDBgwAJcvX8aKFSugVCoxbdo0BAYGYvbs2UhLS0NwcDB0Oh2eeuopGI1G6SLriRMnYtiwYZgxYwZWr14Ns9mMZcuWISkpCRqNBgAwb948vP7661i8eDFmzZqF/fv348MPP0RWVpbUR1paGhITEzF27FjExsZi3bp1qK6ulu6ac6UXujXhKIO2LB9Q2L/oO6Taih+pypG6Nc/hNoYrLmKCBtD5qtqpSyIiIvdqU2D67rvvMG3aNFy9ehV9+vTB/fffjy+++AJ9+vQBAKxduxY+Pj6YMmUKrFYrTCYT1q9fL71fqVRi586dmD9/PoxGI/z9/ZGYmIhVq1ZJNZGRkcjKysKCBQuQkZGBfv364a233pKmFACAqVOn4sqVK1i+fDnMZjOio6Oxe/dumwvB5XqhtlNVXcI+zSL4bbc6rAkBsE/ji/O/2o+GgAi7NdqyQGA7EBKgaadOiYiI3KtNgWnLli1Ox7VaLTIzM5GZmemwZsCAAdi1a5fT7YwfPx6nTp1yWpOcnIzk5OTb6oXaRll3DX4KK4onZMAwKNp+UdnX8Pl4Lob0rAfCHdxJ5+DsFBERkbdq8zxMRFZ9FBAe3dFtkIs0lkLgspOQ6tcL0PNuUCIiZxiYiLqoRm0waoQGhs9SgM+cFKr8gKRjDE1ERE4wMBF1AGdnfTSWQrfsoyEgAvHWV/CnaQMR1cfBGaayr4GP5wI1VxmYiIicYGAi8iBXzvoYANQIDRq1wbe9v8vojbreIx1fT0ZERC5hYCLyIFfO+hReqcLMDy7gDQd3GRIRkecxMFH7KPv61sa6AbmzPnWiApdR4eGuiIjIGQYmci+/Xs0XEX8813mdyq+5tpsqLK26pTEiIuoYDEzkXnpD8x1XNVed13XTW9mD/NXwVSmdzoQOAL4qJYL81Z5pioiIZDEwkfvpDd0yDLkiQu+LfQvHoby63mldkL8aEXpfD3VFRERyGJiIPCxC78swRETUyfh0dANERERE3o6BiYiIiEgGAxMRERGRDF7DRNTFOZumQFtWhSgP9kJE1FkxMBF1Ua5MYTBccRFZGqC0yooQz7VGRNTpMDARdVGuTGFw5Ws1cBCorG1gYCIicoKBiagLk5vCoLCseUxjKQQu23+2HQDXJhq1FHPCUiLqshiYiLqxRm0waoQGhs9SgM+cFKr8mmdwdxR2LMVAZizQUON8h3LbISLyUgxMRN1YQ0AE4q2v4E/TBiKqj4MzTGVfNz8bsOaq46BTc7U5LE1+E+g9+Na3Q0TkpRiYiLq5y+iNut4jgfDA299Y78FAePTtb4eIyMtwHiYiIiIiGQxMRERERDL4lRwRuU3hlSrUiQq7Y5wkk4g6MwYmIrptLRNfpmzJwxkHgYmTZBJRZ8bARES3rWXiy2cmDkGfwbF2azhJJhF1ZgxMROQ2hmBfREXYv9uuZZJMIqLOiBd9ExEREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJOO2AtPvfvc7KBQKpKamSuvq6uqQlJSEXr16ISAgAFOmTEFJSYnN+4qKipCQkAA/Pz+EhIRg0aJFuH79uk3NgQMHMGbMGGg0GkRFRWHTpk2t9p+ZmYk77rgDWq0WcXFxOHbsmM24K70QERERybnlwHT8+HH88Y9/xKhRo2zWL1iwAH//+9+xbds2HDx4EJcvX8bkyZOl8cbGRiQkJKC+vh5HjhzBe++9h02bNmH58uVSzcWLF5GQkIAJEyYgLy8PqampmDNnDvbs2SPVbN26FWlpaVixYgVOnjyJ0aNHw2QyobS01OVeiIiIiFwibsEPP/wgBg0aJPbu3SvGjRsnUlJShBBCWCwWoVKpxLZt26Tac+fOCQAiJydHCCHErl27hI+PjzCbzVLNhg0bhE6nE1arVQghxOLFi8Xw4cNt9jl16lRhMpmk17GxsSIpKUl63djYKMLDw0V6errLvcipqKgQAERFRYVL9V3d+bx/CLFC1/yTuoT87yxiwJKdIv87i+OiS6eEWKFr/umAK58Nfn6IyFPa4+/vWzrDlJSUhISEBMTHx9usz83NRUNDg836oUOHon///sjJyQEA5OTkYOTIkQgNDZVqTCYTKisrcebMGanm5m2bTCZpG/X19cjNzbWp8fHxQXx8vFTjSi83s1qtqKystFmIiIiIerT1DVu2bMHJkydx/PjxVmNmsxlqtRp6vd5mfWhoKMxms1RzY1hqGW8Zc1ZTWVmJ2tpalJeXo7Gx0W7NV1995XIvN0tPT8fKlSud/OmJ6HYVX6tF3aUKh+NB/mpE6H092BERkbw2Babi4mKkpKRg79690Gq17dVTh1m6dCnS0tKk15WVlTAYDB3YEVHXofNVAQB+/2kBzuypd1jnq1Ji38JxDE1E5FXaFJhyc3NRWlqKMWPGSOsaGxtx6NAhvP7669izZw/q6+thsVhszuyUlJQgLCwMABAWFtbqbraWO9durLn5braSkhLodDr4+vpCqVRCqVTarblxG3K93Eyj0UCj0bThiBCRq0ICmv/byng0GnW9R9qtKSytQurWPJRX1zMwEZFXadM1TA8++CDy8/ORl5cnLWPHjsX06dOlf1apVMjOzpbeU1BQgKKiIhiNRgCA0WhEfn6+zd1se/fuhU6nw7Bhw6SaG7fRUtOyDbVajZiYGJuapqYmZGdnSzUxMTGyvRCR50X1CcCIiEC7S1RIQEe3R0RkV5vOMPXs2RMjRoywWefv749evXpJ62fPno20tDQEBwdDp9PhqaeegtFoxD333AMAmDhxIoYNG4YZM2Zg9erVMJvNWLZsGZKSkqSzO/PmzcPrr7+OxYsXY9asWdi/fz8+/PBDZGVlSftNS0tDYmIixo4di9jYWKxbtw7V1dV44oknAACBgYGyvRARERG5os0XfctZu3YtfHx8MGXKFFitVphMJqxfv14aVyqV2LlzJ+bPnw+j0Qh/f38kJiZi1apVUk1kZCSysrKwYMECZGRkoF+/fnjrrbdgMpmkmqlTp+LKlStYvnw5zGYzoqOjsXv3bpsLweV6IaIOUPa1wyFtWRXCUebBZoiIXHPbgenAgQM2r7VaLTIzM5GZmenwPQMGDMCuXbucbnf8+PE4deqU05rk5GQkJyc7HHelFyLyEL9egMoP+Hiuw5IoAPs0GhRX3Q0g0GOtERHJcfsZJiIiu/QGIOkYUHPVYUnx+TwYPkuBsu6aBxsjIpLHwEREnqM3NC8OWK9UAeBcTUTkfRiYiMhrcK4mIvJWDExE5DU4VxMReSsGJiLyOlF9AoBwXvRNRN7jlh6+S0RERNSdMDARERERyeBXckSEwtIqh2PasipEebAXIiJvxMBE1I0F+avhq1IidWuew5rhiovI0gClVVaEeK41IiKvwsBE1I1F6H2xb+E4lFc7voX/ytdq4CBQWdvAwERE3RYDE1E3F6H3dXp7fmEZb90nIuJF30REREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIN3yRGRSzSWQuBygOMxIqIujIGJiJxq1AajRmhg+CwF+Mx+jQFAjdCgURvs0d6IiDyFgYmInGoIiEC89RX8adpARPWxf4ap8EoVZn5wAW8ERHi4OyIiz2BgIiJZl9Ebdb1HAuGBdsfrRAUuo8LDXREReQ4v+iYiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDF70TUQuKSytuqUxIqKugIGJiJwK8lfDV6VE6tY8p3W+KiWC/NWeaYqIyMMYmIjIqQi9L/YtHIfy6nqndUH+akTofT3UlZtYioGaq85r/HoBeoNn+iEir8XARESyIvS+ng1DZV87HNKWVSEcZbe/D0sxkBkLNNQ4r1P5AUnHGJqIujkGJiLyHn69mgPKx3MdlkQB2KfRoLjqbgD2J9J0Sc3V5rA0+U2g92D7NWVfN/dSc5WBiaibY2AiIu+hNzSfzXHyNVnx+TwYPkuBsu6ae/bZezAQHu2ebRFRl8XARJJLllqn16lcuVaLKA/2Q92U3uD0bI71Cu/IIyLPY2AiAM1hKX7NQdQ2NDqsGa64iAkaQOer8mBnREREHY+BiQAA5dX1qG1oxLqp0YgKsf9Eem1ZILAdCAnQeLg7IiKijsXARDaiQgIwIsLBhbQK+0GKiIioq+OjUYiIiIhkMDARERERyWBgIiIiIpLBwEREREQko02BacOGDRg1ahR0Oh10Oh2MRiM++eQTabyurg5JSUno1asXAgICMGXKFJSUlNhso6ioCAkJCfDz80NISAgWLVqE69ev29QcOHAAY8aMgUajQVRUFDZt2tSql8zMTNxxxx3QarWIi4vDsWPHbMZd6aU7uWSpxelLFQ4XPm2eiIjIsTbdJdevXz/87ne/w6BBgyCEwHvvvYf//u//xqlTpzB8+HAsWLAAWVlZ2LZtGwIDA5GcnIzJkyfj8OHDAIDGxkYkJCQgLCwMR44cwffff4+ZM2dCpVLhpZdeAgBcvHgRCQkJmDdvHt5//31kZ2djzpw56Nu3L0wmEwBg69atSEtLw8aNGxEXF4d169bBZDKhoKAAISEhACDbS3fiyhxLAJ82T0RE5JC4TUFBQeKtt94SFotFqFQqsW3bNmns3LlzAoDIyckRQgixa9cu4ePjI8xms1SzYcMGodPphNVqFUIIsXjxYjF8+HCbfUydOlWYTCbpdWxsrEhKSpJeNzY2ivDwcJGeni6EEC714oqKigoBQFRUVLj8Hm+U/51FDFiyU2w/+Z3I/87icPmuvMb5hi6dEmKFrvknUQc5n/cPIVbomn/eDlc+z/zME3VK7fH39y1fw9TY2IgtW7aguroaRqMRubm5aGhoQHx8vFQzdOhQ9O/fHzk5OQCAnJwcjBw5EqGhoVKNyWRCZWUlzpw5I9XcuI2WmpZt1NfXIzc316bGx8cH8fHxUo0rvdhjtVpRWVlps3QlLXMsOVo8+jR6IiKiTqTNgSk/Px8BAQHQaDSYN28etm/fjmHDhsFsNkOtVkOv19vUh4aGwmw2AwDMZrNNWGoZbxlzVlNZWYna2lqUlZWhsbHRbs2N25DrxZ709HQEBgZKi8HAp5MTERHRLQSmIUOGIC8vD0ePHsX8+fORmJiIs2fPtkdvHrd06VJUVFRIS3FxcUe3RERERF6gzY9GUavViIpqfmZ9TEwMjh8/joyMDEydOhX19fWwWCw2Z3ZKSkoQFhYGAAgLC2t1N1vLnWs31tx8N1tJSQl0Oh18fX2hVCqhVCrt1ty4Dble7NFoNNBo+Jw0IiIisnXb8zA1NTXBarUiJiYGKpUK2dnZ0lhBQQGKiopgNBoBAEajEfn5+SgtLZVq9u7dC51Oh2HDhkk1N26jpaZlG2q1GjExMTY1TU1NyM7Olmpc6YXssBQDl/McL2Vfd2BzRO2j8EqV4+k2rnC6DSJq1qYzTEuXLsXPfvYz9O/fHz/88AM2b96MAwcOYM+ePQgMDMTs2bORlpaG4OBg6HQ6PPXUUzAajbjnnnsAABMnTsSwYcMwY8YMrF69GmazGcuWLUNSUpJ0ZmfevHl4/fXXsXjxYsyaNQv79+/Hhx9+iKysLKmPtLQ0JCYmYuzYsYiNjcW6detQXV2NJ554AgBc6oVuYikGMmOBhhrndSo/wK+XZ3oiakelVVaEAEjZkoczosJuzXDFRWRp/lNLRN1XmwJTaWkpZs6cie+//x6BgYEYNWoU9uzZg4ceeggAsHbtWvj4+GDKlCmwWq0wmUxYv3699H6lUomdO3di/vz5MBqN8Pf3R2JiIlatWiXVREZGIisrCwsWLEBGRgb69euHt956S5qDCQCmTp2KK1euYPny5TCbzYiOjsbu3bttLgSX64VuUnO1OSxNfhPoPdhxnV8vQM+L4anzq6xtQAiAZyYOQZ/BsXZrrnytBg7+p5aIuq82Baa3337b6bhWq0VmZiYyMzMd1gwYMAC7du1yup3x48fj1KlTTmuSk5ORnJx8W72QHb0HA+HRHd0FkccYgn0RFRFod6ywjFNtEFEzPkuOiIiISAYDExEREZEMBiYiIiIiGQxMRERERDLaPHEldU7hKIO2LB9QBNgv4BxLREREDjEwdQOqqkvYp1kEv+1WmULOsURERGQPA1M3oKy7Bj+FFcUTMmAYFO24kHMsERER2cXA1I1Y9VGcY4mIiOgW8KJvIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhkMTEREREQyGJiIiIiIZDAwEREREclgYCIiIiKSwcBEREREJIOBiYiIiEgGAxMRERGRDAYmIiIiIhk9OroBIiJvV3ytFnWXKhyOB/mrEaH39WBHRORpDExE1Cl5IsTofFUAgN9/WoAze+od1vmqlNi3cBxDE1EXxsBERJ2KJ0NMSIAGALDxpwGw6gPt1hRfq8Vv9lxGeXU9AxNRF8bARESdSkuIyXg0GnW9R9qtKSytQurWvNsPMX69AJUfDJ+lOCyJArBPo0Fx1d0A7IcqIur8GJiIqFOK6hMAhLdzQNEbgKRjQM1VhyXF5/Ng+CwFyrpr7dsLEXWoNt0ll56ejrvvvhs9e/ZESEgIJk2ahIKCApuauro6JCUloVevXggICMCUKVNQUlJiU1NUVISEhAT4+fkhJCQEixYtwvXr121qDhw4gDFjxkCj0SAqKgqbNm1q1U9mZibuuOMOaLVaxMXF4dixY23uhYjIKb0BCI92uFj1UR3ZHRF5SJsC08GDB5GUlIQvvvgCe/fuRUNDAyZOnIjq6mqpZsGCBfj73/+Obdu24eDBg7h8+TImT54sjTc2NiIhIQH19fU4cuQI3nvvPWzatAnLly+Xai5evIiEhARMmDABeXl5SE1NxZw5c7Bnzx6pZuvWrUhLS8OKFStw8uRJjB49GiaTCaWlpS73QkREROQScRtKS0sFAHHw4EEhhBAWi0WoVCqxbds2qebcuXMCgMjJyRFCCLFr1y7h4+MjzGazVLNhwwah0+mE1WoVQgixePFiMXz4cJt9TZ06VZhMJul1bGysSEpKkl43NjaK8PBwkZ6e7nIvcioqKgQAUVFR4VK9tzqf9w8hVuiafxJ1dpdOCbFC1/zTgfzvLGLAkp0i/zuLwxp3/XfB/76IvE97/P19WxNXVlQ039IbHBwMAMjNzUVDQwPi4+OlmqFDh6J///7IyckBAOTk5GDkyJEIDQ2VakwmEyorK3HmzBmp5sZttNS0bKO+vh65ubk2NT4+PoiPj5dqXOnlZlarFZWVlTYLERER0S0HpqamJqSmpuK+++7DiBEjAABmsxlqtRp6vd6mNjQ0FGazWaq5MSy1jLeMOauprKxEbW0tysrK0NjYaLfmxm3I9XKz9PR0BAYGSovBYHDxaBAREVFXdsuBKSkpCadPn8aWLVvc2U+HWrp0KSoqKqSluLi4o1siIiIiL3BL0wokJydj586dOHToEPr16yetDwsLQ319PSwWi82ZnZKSEoSFhUk1N9/N1nLn2o01N9/NVlJSAp1OB19fXyiVSiiVSrs1N25DrpebaTQaaDSaNhwJIiIi6g7adIZJCIHk5GRs374d+/fvR2RkpM14TEwMVCoVsrOzpXUFBQUoKiqC0WgEABiNRuTn59vczbZ3717odDoMGzZMqrlxGy01LdtQq9WIiYmxqWlqakJ2drZU40ovRERERK5o0xmmpKQkbN68GX/961/Rs2dP6VqgwMBA+Pr6IjAwELNnz0ZaWhqCg4Oh0+nw1FNPwWg04p577gEATJw4EcOGDcOMGTOwevVqmM1mLFu2DElJSdLZnXnz5uH111/H4sWLMWvWLOzfvx8ffvghsrKypF7S0tKQmJiIsWPHIjY2FuvWrUN1dTWeeOIJqSe5XoiIiIhc0abAtGHDBgDA+PHjbda/++67ePzxxwEAa9euhY+PD6ZMmQKr1QqTyYT169dLtUqlEjt37sT8+fNhNBrh7++PxMRErFq1SqqJjIxEVlYWFixYgIyMDPTr1w9vvfUWTCaTVDN16lRcuXIFy5cvh9lsRnR0NHbv3m1zIbhcL0RERESuaFNgEkLI1mi1WmRmZiIzM9NhzYABA7Br1y6n2xk/fjxOnTrltCY5ORnJycm31QsRERGRnNuah4mIiIioO+DDd4moSwpHGbRl+YAiwO64xlLo4Y6IqDNjYCKiLkdVdQn7NIvgt93qsMYAoEZo0KgN9lxjRNRpMTARUZejrLsGP4UVxRMyYBgUbbem8EoVZn5wAW8ERHi2OSLqlBiYiKjLsuqjgPBou2N1ogKXUeHZhoio02JgIqLOqexrh0O8PomI3I2BiYg6F79egMoP+HiuwxJen0RE7sbARESdi94AJB0Daq46LOH1SUTkbgxMRNT56A3NiwO8PomI3I0TVxIRERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJ4EzfRERuUHytFnWXHM8uHuSvRoTe14MdEZE7MTARUZdVWFp1S2NtofNVAQB+/2kBzuypd1jnq1Ji38JxDE1EnRQDExF1OUH+aviqlEjdmue0zlelRJC/+rb2FRKgAQBkPBqNut4j7dYUllYhdWseyqvrGZiIOikGJiLqciL0vti3cBzKqx2f8QHc+zVZVJ8AIDzQLdsiIu/DwEREXVKE3pdnc4jIbXiXHBEREZEMBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBwEREREQkg4GJiIiISAYDExEREZEMBiYiIiIiGXw0ShdwyVLr9JlZV67VIsqD/RAREXU1DEyd3CVLLeLXHERtQ6PDmuGKi5igAXS+Kg92RtTNlH3tcEhbVoVwlHmwGSJyNwamTq68uh61DY1YNzUaUSEBdmu0ZYHAdiAkQOPh7oi6Ab9egMoP+Hiuw5IoAPs0GhRX3Q0g0GGd3NliAAjyV/OhwkQdgIGpi4gKCcCICAe/iBX2gxQRuYHeACQdA2quOiwpPp8Hw2cpUNZdc1jjytliAPBVKbFv4TiGJiIPa/NF34cOHcIvfvELhIeHQ6FQYMeOHTbjQggsX74cffv2ha+vL+Lj43H+/HmbmmvXrmH69OnQ6XTQ6/WYPXs2qqqqbGq+/PJLPPDAA9BqtTAYDFi9enWrXrZt24ahQ4dCq9Vi5MiR2LVrV5t7ISK6bXoDEB7tcLHq5a8ivPFs8c6n7re7rJsajdqGRtmzUETkfm0OTNXV1Rg9ejQyMzPtjq9evRqvvvoqNm7ciKNHj8Lf3x8mkwl1dXVSzfTp03HmzBns3bsXO3fuxKFDh/Dkk09K45WVlZg4cSIGDBiA3NxcvPLKK3jhhRfwxhtvSDVHjhzBtGnTMHv2bJw6dQqTJk3CpEmTcPr06Tb10hWEowzasnzgcp79xcm1FUTkXVrOFttbHH3tTkQeIG4DALF9+3bpdVNTkwgLCxOvvPKKtM5isQiNRiM++OADIYQQZ8+eFQDE8ePHpZpPPvlEKBQKcenSJSGEEOvXrxdBQUHCarVKNUuWLBFDhgyRXj/yyCMiISHBpp+4uDjx61//2uVe5FRUVAgAoqKiwqX6jvDVV2dE9fI+QqzQOV9+GyZEeVFHt0vULZ3P+4cQK3TNPx3I/84iBizZKfK/s9xWDRG1z9/fbr2G6eLFizCbzYiPj5fWBQYGIi4uDjk5OXj00UeRk5MDvV6PsWPHSjXx8fHw8fHB0aNH8ctf/hI5OTn48Y9/DLVaLdWYTCa8/PLLKC8vR1BQEHJycpCWlmazf5PJJH1F6EovXYGy7hr8FFYUT8iAYVC040K/Xs1fGxARATAXnUdVeYnTmoCgUIT1H+Shjoi8m1sDk9lsBgCEhobarA8NDZXGzGYzQkJCbJvo0QPBwcE2NZGRka220TIWFBQEs9ksux+5Xm5mtVphtVql15WVlTJ/Yu9h1Uc1Xy9BRCTDXHQeurfvQ5jC6rSuRmhgnn2YoYkIvEvORnp6OlauXNnRbRARtauq8hKEKaw4MeZl6AeMsFtj+fY0xp5cgsvlJQADE5F7A1NYWBgAoKSkBH379pXWl5SUIDo6WqopLS21ed/169dx7do16f1hYWEoKbE9VdzyWq7mxnG5Xm62dOlSm6/5KisrYTDwaywi6pr0A0YgavT9dscKAeCkR9sh8mpufZZcZGQkwsLCkJ2dLa2rrKzE0aNHYTQaAQBGoxEWiwW5ublSzf79+9HU1IS4uDip5tChQ2hoaJBq9u7diyFDhiAoKEiquXE/LTUt+3Gll5tpNBrodDqbhYiIiKjNgamqqgp5eXnIy8sD0HxxdV5eHoqKiqBQKJCamorf/va3+Nvf/ob8/HzMnDkT4eHhmDRpEgDgzjvvxE9/+lPMnTsXx44dw+HDh5GcnIxHH30U4eHhAIDHHnsMarUas2fPxpkzZ7B161ZkZGTYnP1JSUnB7t27sWbNGnz11Vd44YUXcOLECSQnJwOAS70QERERuaLNX8mdOHECEyZMkF63hJjExERs2rQJixcvRnV1NZ588klYLBbcf//92L17N7RarfSe999/H8nJyXjwwQfh4+ODKVOm4NVXX5XGAwMD8emnnyIpKQkxMTHo3bs3li9fbjNX07333ovNmzdj2bJleO655zBo0CDs2LEDI0b85/t4V3ohIiIiktPmwDR+/HgIIRyOKxQKrFq1CqtWrXJYExwcjM2bNzvdz6hRo/CPf/zDac3DDz+Mhx9++LZ6ISIiIpLj1muYiIiIiLoiBiYiIiIiGQxMRERERDIYmIiIiIhkMDARERERyWBgIiIiIpLBZ8kREXmJcJRBW5YPKALsjmvLqhCOMg93RUQAAxMRkVdQVV3CPs0i+G23OqyJArBPo0Fx1d0AAj3WGxExMBEReQVl3TX4KawonpABw6BouzXF5/Ng+CwFyrprnm2OiBiYiIg8pfhaLeouVdgdu3KtFlEArPooIDzabo31SlX7NUdETjEweblLllqUV9c7HG/5JUtE3kvnqwIA/P7TApzZY/+/5+GKi5ig+U8tEXkXBiYvdslSi/g1B1Hb0Oiwhr9kibxfSIAGALDxpwGw6u1fe6SxBACf/afWGWdnqgAgyF+NCL3vrTXbXizFQM1V5zV+vQC9wTP9ELURA5MXK6+uR21DI9ZNjUZUiKO7ZgKB7a79kiWiDuLXC1D5wfBZivM6lV9zrQOunKkCAF+VEvsWjvOe0GQpRtPrd8Pneq3TsqYevvBJPs7QRF6JgakTiAoJwIgIB3fEOLj9mIi8iN4AJB277TMsLf9jlPFoNOp6j7RbU1hahdSteSivrveawFRaehkh12uRUv+/KBQRdmuiFJeQgfXNtQxM5IUYmIiIPEFvcNuZkyjFZcdzNfl431xNlbUNCAEw6aGfoM/gWLs1V74+BhxcL9USeRsGJi8nN5Edyr72bENE1HH+/dUePp7rsMSb52oyBPsiysHZ8sIy7zgbRuQIA5MXc2Uiu+ZC59c9EFEX4cJXey1zNf1QcAiFDuZrsnx7ur06JOqyGJi8mCsT2QHgnSVE3YnMV3uq6/6o2a/B2JNLgJOON1MjNAgICm2HBom6JgamTsDZRHZERDcK6z8I5tmHcbm8xGldQFAowvoP8lBXRJ0fAxMRURcT1n8QwDBE5FYMTERE5JDcJJmAl06USeRmDExERNRKyySZO/buR+GnBU5ra3vosWKGCb381XbH+Qgn6goYmIiIqJWQkHA09fBFBtbL1tYIDeZtSsVVobM7HqW4hAlqPsKJOjcGJiIiak1vaH5Midzs5DVl0G75Ff6keNlpWVMPX4SEhLuxQSLPYmAiIiL7XJyd3JVg5cPpT6iTY2AiIqLb48bHvhB5K5+OboCIiIjI2zEwEREREclgYCIiIiKSwWuYiIiI7DAXnUcVHzFD/8bAREREdBNz0Xno3r4PYQqr07oaoYF59mGGpm6AgYmIiOgmVeUlCFNYcWLMy9APGGG3xvLtaYw9uaT5QccMTF0eAxMREZED+gEjEDX6frtjhQBw0qPtUAfiRd9EREREMhiYiIiIiGTwK7kOdMlSi/LqeofjfMI3ERGRd2Bg6iCXLLWYseYj+F63OKzhE76JqLuxfHu6+dogB1y5jd+V6QBc6cPbcJqDjsXA1EGqSi5ip08a/DTOb1nlE76JqDsICApFjdBg7MklTi+klruN39XpAFxRIzQICAq97e24gzdOc9DdAly3CEyZmZl45ZVXYDabMXr0aLz22muIjY3t0J6Uddfgp7CieEIGDIOiHdbxCd9E1B2E9R8E8+zDzbfoO+DKbfyuTAfgKnf9Ze+OYOFt0xx4Y4Brb10+MG3duhVpaWnYuHEj4uLisG7dOphMJhQUFCAkJKSj24NVHwWER3d0G0REHS6s/yCnf9G35TZ+Z9MBeJK7g4W3THPgbQHOE7p8YPrDH/6AuXPn4oknngAAbNy4EVlZWXjnnXfw7LPPdnB3RETUlXX1YOEtAc4TunRgqq+vR25uLpYuXSqt8/HxQXx8PHJyclrVW61WWK3/+b+AiooKAEBlZaXbe/uhqhqVVtH8sx22T0TU1bT83vzu3HH8UFVtt6ai+CxCPPS7tS39KHtFIiRy1G1vx9mfy5XtuIs7+9H3jkCvvv3d2l9LT0II921UdGGXLl0SAMSRI0ds1i9atEjExsa2ql+xYoUAwIULFy5cuHDpAktxcbHbMkWXPsPUVkuXLkVaWpr0uqmpCdeuXUOvXr2gUCicvreyshIGgwHFxcXQ6XTt3WqnwGPSGo9JazwmrfGY2Mfj0hqPSWstx+Ts2bMID3ffXeZdOjD17t0bSqUSJSW2dyeUlJQgLCysVb1Go4FGo7FZp9fr27RPnU7HD+1NeExa4zFpjcekNR4T+3hcWuMxaS0iIgI+Pu57oEmXfjSKWq1GTEwMsrOzpXVNTU3Izs6G0WjswM6IiIioM+nSZ5gAIC0tDYmJiRg7dixiY2Oxbt06VFdXS3fNEREREcnp8oFp6tSpuHLlCpYvXw6z2Yzo6Gjs3r0boaHunb1Vo9FgxYoVrb7S6854TFrjMWmNx6Q1HhP7eFxa4zFprb2OiUIId95zR0RERNT1dOlrmIiIiIjcgYGJiIiISAYDExEREZEMBiYiIiIiGQxMLsrMzMQdd9wBrVaLuLg4HDt2zGn9tm3bMHToUGi1WowcORK7du3yUKee1ZbjsmnTJigUCptFq9V6sNv2d+jQIfziF79AeHg4FAoFduzYIfueAwcOYMyYMdBoNIiKisKmTZvavU9PausxOXDgQKvPiUKhgNls9kzD7Sw9PR133303evbsiZCQEEyaNAkFBQWy7+vqv1Nu5bh09d8pGzZswKhRo6RJKY1GIz755BOn7+nqn5O2HhN3fkYYmFywdetWpKWlYcWKFTh58iRGjx4Nk8mE0tJSu/VHjhzBtGnTMHv2bJw6dQqTJk3CpEmTcPr0aQ933r7aelyA5tlov//+e2n59ttvPdhx+6uursbo0aORmZnpUv3FixeRkJCACRMmIC8vD6mpqZgzZw727NnTzp16TluPSYuCggKbz0pISEg7dehZBw8eRFJSEr744gvs3bsXDQ0NmDhxIqqrHT+ctDv8TrmV4wJ07d8p/fr1w+9+9zvk5ubixIkT+MlPfoL//u//xpkzZ+zWd4fPSVuPCeDGz4jbnkrXhcXGxoqkpCTpdWNjowgPDxfp6el26x955BGRkJBgsy4uLk78+te/btc+Pa2tx+Xdd98VgYGBHuqu4wEQ27dvd1qzePFiMXz4cJt1U6dOFSaTqR076ziuHJPPPvtMABDl5eUe6amjlZaWCgDi4MGDDmu6y++UG7lyXLrb7xQhhAgKChJvvfWW3bHu+DkRwvkxcednhGeYZNTX1yM3Nxfx8fHSOh8fH8THxyMnJ8fue3JycmzqAcBkMjms74xu5bgAQFVVFQYMGACDwSD7fwXdQXf4rNyq6Oho9O3bFw899BAOHz7c0e20m4qKCgBAcHCww5ru+Dlx5bgA3ed3SmNjI7Zs2YLq6mqHj/bqbp8TV44J4L7PCAOTjLKyMjQ2NraaGTw0NNThNRVms7lN9Z3RrRyXIUOG4J133sFf//pX/OUvf0FTUxPuvfdefPfdd55o2Ss5+qxUVlaitra2g7rqWH379sXGjRvx0Ucf4aOPPoLBYMD48eNx8uTJjm7N7ZqampCamor77rsPI0aMcFjXHX6n3MjV49Idfqfk5+cjICAAGo0G8+bNw/bt2zFs2DC7td3lc9KWY+LOz0iXfzQKeQ+j0WjzfwH33nsv7rzzTvzxj3/Eiy++2IGdkTcZMmQIhgwZIr2+9957ceHCBaxduxZ//vOfO7Az90tKSsLp06fx+eefd3QrXsXV49IdfqcMGTIEeXl5qKiowP/7f/8PiYmJOHjwoMOA0B205Zi48zPCwCSjd+/eUCqVKCkpsVlfUlKCsLAwu+8JCwtrU31ndCvH5WYqlQp33XUXCgsL26PFTsHRZ0Wn08HX17eDuvI+sbGxXS5UJCcnY+fOnTh06BD69evntLY7/E5p0ZbjcrOu+DtFrVYjKioKABATE4Pjx48jIyMDf/zjH1vVdpfPSVuOyc1u5zPCr+RkqNVqxMTEIDs7W1rX1NSE7Oxsh9+ZGo1Gm3oA2Lt3r9PvWDubWzkuN2tsbER+fj769u3bXm16ve7wWXGHvLy8LvM5EUIgOTkZ27dvx/79+xEZGSn7nu7wObmV43Kz7vA7pampCVar1e5Yd/ic2OPsmNzstj4jbrl0vIvbsmWL0Gg0YtOmTeLs2bPiySefFHq9XpjNZiGEEDNmzBDPPvusVH/48GHRo0cP8fvf/16cO3dOrFixQqhUKpGfn99Rf4R20dbjsnLlSrFnzx5x4cIFkZubKx599FGh1WrFmTNnOuqP4HY//PCDOHXqlDh16pQAIP7whz+IU6dOiW+//VYIIcSzzz4rZsyYIdX/61//En5+fmLRokXi3LlzIjMzUyiVSrF79+6O+iO4XVuPydq1a8WOHTvE+fPnRX5+vkhJSRE+Pj5i3759HfVHcKv58+eLwMBAceDAAfH9999LS01NjVTTHX+n3Mpx6eq/U5599llx8OBBcfHiRfHll1+KZ599VigUCvHpp58KIbrn56Stx8SdnxEGJhe99tpron///kKtVovY2FjxxRdfSGPjxo0TiYmJNvUffvihGDx4sFCr1WL48OEiKyvLwx17RluOS2pqqlQbGhoq/uu//kucPHmyA7puPy23xN+8tByHxMREMW7cuFbviY6OFmq1WvzoRz8S7777rsf7bk9tPSYvv/yyGDhwoNBqtSI4OFiMHz9e7N+/v2Oabwf2jgUAm3/v3fF3yq0cl67+O2XWrFliwIABQq1Wiz59+ogHH3xQCgZCdM/PSVuPiTs/IwohhGj7eSkiIiKi7oPXMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIBgMTERERkQwGJiIiIiIZDExEREREMhiYiIiIiGQwMBERERHJYGAiIiIiksHARERERCSDgYmIiIhIxv8Hg1Pka6FPfPEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98046bb5-db38-4730-869b-2c6512754b36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "f703cd30-fc17-4e4e-a458-40d9c74b266b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 3156 overPopped HDs to within 5959\n",
      "working on squaring down HD 0 time is now 0\n",
      "working on squaring down HD 603 time is now 1\n",
      "working on squaring down HD 1095 time is now 3\n",
      "working on squaring down HD 1518 time is now 3\n",
      "working on squaring down HD 1984 time is now 4\n",
      "working on squaring down HD 2597 time is now 6\n",
      "working on squaring down HD 3083 time is now 7\n",
      "working on squaring down HD 3591 time is now 8\n",
      "working on squaring down HD 4298 time is now 9\n",
      "working on squaring down HD 4832 time is now 10\n",
      "working on squaring down HD 5531 time is now 11\n",
      "working on squaring down HD 6117 time is now 13\n",
      "working on squaring down HD 6556 time is now 14\n",
      "working on squaring down HD 7140 time is now 15\n",
      "working on squaring down HD 7830 time is now 16\n",
      "working on squaring down HD 8506 time is now 17\n",
      "We have attempted to address overpop in a total of 3156 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList, stillOverPoppedList = list(), list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = max(0.9 * np.median(unitPop), aDP - minDistrictPop)  # = aDP - minDistrictPop   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%200 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = homeU[t] #HDunitList[t][distList.index(np.min(distList))] #update from vanillaHD\n",
    "        barredSet = barredJettisonSet.union({starterU})\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )        \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            stillOverPoppedList.append(t)\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "        if homeU[t] not in HDunitList[t]:\n",
    "            print(\"WARNING: we lost the home unit\",homeU[t],\"from HD\",HD)\n",
    "            lostHomeUlist.append(t)\n",
    "            \n",
    "print(\"We have attempted to address overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "if len(stillOverPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillOverPoppedList))\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "9c4d12b3-8596-4a5b-937e-7ef8ed71a903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 0 underpopped HDs and 3 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "68490ecd-8d53-44f0-b7fd-0df71cce9d27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intermediate save to file -- still to address under-overpops\n"
     ]
    }
   ],
   "source": [
    "print(\"intermediate save to file -- still to address under-overpops\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC} )\n",
    "outname = STATE+str(int(nHDs))+\"_99COI1850_3overpopped.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6d416882-ff8c-461c-ae49-2b8212257f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/OH99COI1850-unitTopologiesBG_12Dec.csv ./state_map_files/OH99COI1850-unitTopologiesBG_12Dec.csv\n",
      "enter the number of districts in the state map 99\n",
      "enter the ratio of minDistrictPop to aDP; e.g. 0.95 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 119186.343 people in each of the 99 districts in the state map\n"
     ]
    }
   ],
   "source": [
    "#on cold restart, read in the unit topology\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/OH99COI1850-unitTopologiesBG_12Dec.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "cantSplit = topDF[\"cantSplit\"]\n",
    "unitCountyNo = topDF[\"unitCountyNo\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "borderUnits = list(borderSet) #nomenclature\n",
    "\n",
    "nbrListString = topDF[\"unitNbrs\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "uTLstring = topDF[\"unitTractList\"]\n",
    "unitTractList = [ast.literal_eval(uTLstring[u]) for u in range(nUnits)]\n",
    "tractUnitNo = [-999]*nTracts\n",
    "for u in range(nUnits):\n",
    "    for b in unitTractList[u]:\n",
    "        tractUnitNo[b] = u\n",
    "\n",
    "allUnits = [u for u in range(nUnits)] #topDF[\"allUnits\"]\n",
    "nDistricts = int(input(\"enter the number of districts in the state map\"))\n",
    "statePop = np.sum(unitPop)\n",
    "aDP = statePop / float(nDistricts)\n",
    "minDPR = float(input(\"enter the ratio of minDistrictPop to aDP; e.g. 0.95\"))\n",
    "minDistrictPop = minDPR * aDP\n",
    "maxDistrictPop = 2*aDP - minDistrictPop\n",
    "print(\"there are\",r3(aDP),\"people in each of the\",nDistricts,\"districts in the state map\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc37354b-010b-40a6-9703-98f19e4b204c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optional restart.  Read in unpatched HDunit lists\n",
      "I read in a total of 8941 home district unit lists, of which 8934 are populated\n"
     ]
    }
   ],
   "source": [
    "############ OPTIONAL RESTART *******\n",
    "print(\"optional restart.  Read in unpatched HDunit lists\")\n",
    "inDF = pd.read_csv(\"./2024state_HD_output/OH8941_99COI1850_3overpopped.csv\") #OH8941_15COI1850underP.csv\")\n",
    "## note: for this ipynb, we restarted after all but 3 HDs have good pops, but before patching started\n",
    "HDweight, homeU, homeC = inDF[\"HDweight\"],inDF[\"homeU\"],inDF[\"homeC\"]\n",
    "nHDs = len(HDweight)\n",
    "if \"splitUnitNo\" in inDF.columns.values:\n",
    "    splitUnitNo, splitUnitFrac = inDF[\"splitUnitNo\"],inDF[\"splitUnitFrac\"]\n",
    "else:\n",
    "    splitUnitNo, splitUnitFrac = [-999]*nHDs,[0.]*nHDs\n",
    "hasSplitUnit = list()\n",
    "popHDlist = list()\n",
    "for h in range(nHDs):\n",
    "    if HDweight[h] > 0:\n",
    "        popHDlist.append(h)\n",
    "    if splitUnitNo[h] >= 0:\n",
    "        hasSplitUnit.append(h)\n",
    "unitString = inDF[\"HDunitList\"]\n",
    "HDunitList = [ast.literal_eval(unitString[h]) for h in range(nHDs)]\n",
    "print(\"I read in a total of\",nHDs,\"home district unit lists, of which\",len(popHDlist),\"are populated\")\n",
    "HDvPop = inDF[\"HDvPop\"].to_numpy()\n",
    "HDCPx, HDCPy = inDF[\"centroid x\"],inDF[\"centroid y\"]\n",
    "HDCP = [Point(HDCPx[h],HDCPy[h]) for h in range(nHDs)]\n",
    "hdCP = HDCP.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "baddcca0-3001-483d-9a3f-99500f46a069",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "on restart, rebuild the unit geoms\n",
      "rebuilding geom for unit 0\n",
      "rebuilding geom for unit 500\n",
      "rebuilding geom for unit 1000\n",
      "rebuilding geom for unit 1500\n",
      "rebuilding geom for unit 2000\n",
      "rebuilding geom for unit 2500\n",
      "rebuilding geom for unit 3000\n"
     ]
    }
   ],
   "source": [
    "print(\"on restart, rebuild the unit geoms\")\n",
    "unitGeom = [tractGeom[unitTractList[u][0]] for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"rebuilding geom for unit\",u)\n",
    "    for b in unitTractList[u]:\n",
    "        unitGeom[u] = unitGeom[u].union(tractGeom[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "176f2030-6581-49cd-bc88-0b45ffbc325d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "working avg precinct-to-HDcP distance for HD 6400\n",
      "working avg precinct-to-HDcP distance for HD 7200\n",
      "working avg precinct-to-HDcP distance for HD 8000\n",
      "working avg precinct-to-HDcP distance for HD 8800\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 congrl HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d332f67-2479-426e-ab55-1aded5c70535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1816 1.177 pop/aDP\n",
      "3708 1.177 pop/aDP\n",
      "4186 1.159 pop/aDP\n"
     ]
    }
   ],
   "source": [
    "stillOver = list()\n",
    "for t in range(nHDs):\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "        print(t,r3(HDvPop[t]/aDP),\"pop/aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b4a253dd-8e35-4a02-bc7f-c3f6dc01b64c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1816 has pop/aDP 1.1765022397657925 . -faint = unused homeU neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replotting for 1816 with unit nos and indicating homeU\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3708 has pop/aDP 1.1765022397657925 . -faint = unused homeU neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replotting for 3708 with unit nos and indicating homeU\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4186 has pop/aDP 1.158706576782236 . -faint = unused homeU neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replotting for 4186 with unit nos and indicating homeU\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in stillOver:\n",
    "    print(t,\"has pop/aDP\",HDvPop[t]/aDP,\". -faint = unused homeU neighbors\")\n",
    "    plotPoly(HDCP[t].buffer(0.1))\n",
    "    plotCenter(t,HDCP[t])\n",
    "    for u in HDunitList[t]:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(r3(unitPop[u]/aDP),unitGeom[u],5)\n",
    "    for uu in unitNbrs[homeU[t]]:\n",
    "        if uu not in HDunitList[t]:\n",
    "            plotPoly(unitGeom[uu],0.2)\n",
    "            plotCenter(r3(-1.*unitPop[uu]/aDP),unitGeom[uu],9)\n",
    "    plt.show()\n",
    "    print(\"replotting for\",t,\"with unit nos and indicating homeU\")\n",
    "    plotPoly(HDCP[t].buffer(0.1))\n",
    "    plotCenter(t,HDCP[t])\n",
    "    for u in HDunitList[t]:\n",
    "        plotPoly(unitGeom[u])\n",
    "        if u == homeU[t]:\n",
    "            plotCenter(\"h\"+str(u),unitGeom[u],12)\n",
    "        else:\n",
    "            plotCenter(u,unitGeom[u],7)\n",
    "    for uu in unitNbrs[homeU[t]]:\n",
    "        if uu not in HDunitList[t]:\n",
    "            plotPoly(unitGeom[uu],0.2)\n",
    "            plotCenter(-1*uu,unitGeom[uu],9)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f1af809-e31a-4336-ba64-b02bc7e8a6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1254, 1286, 551]\n"
     ]
    }
   ],
   "source": [
    "print(unitNbrs[2461])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "988eef4f-ed05-44a7-ac07-0377320856b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1816 [897, 3436, 1280, 3047, 590, 755, 404, 2076, 1853, 2105, 2492, 1757, 3159]\n",
      "101910\n",
      "614 0.8711400736712429\n",
      "2043 0.8910584630738658\n",
      "941 0.9607560455370454\n",
      "unbroken and noEnclave for proposed modified unit list for HD 1816 are True True\n",
      "(True, True)\n",
      "it is a go.  modify unit list and pop\n",
      "3708 [897, 3436, 1280, 3047, 590, 755, 404, 2076, 1853, 2105, 2492, 1757, 3159]\n",
      "101910\n",
      "614 0.8711400736712429\n",
      "2043 0.8910584630738658\n",
      "941 0.9607560455370454\n",
      "unbroken and noEnclave for proposed modified unit list for HD 3708 are True True\n",
      "(True, True)\n",
      "it is a go.  modify unit list and pop\n"
     ]
    }
   ],
   "source": [
    "for t in stillOver[0:2]:\n",
    "    testList = HDunitList[t].copy()\n",
    "    print(t,testList)\n",
    "    testSet = set(testList).difference({897})\n",
    "    testPop = np.sum([unitPop[u] for u in testSet])\n",
    "    print(testPop)\n",
    "    addSet = {2043, 703, 941,614, 942}\n",
    "    while testPop < minDistrictPop and len(addSet) > 0:\n",
    "        addList = list(addSet)\n",
    "        testSet.add(addList[0])\n",
    "        testPop = np.sum([unitPop[u] for u in testSet])\n",
    "        print(addList[0],testPop/aDP)\n",
    "        addSet.remove(addList[0])\n",
    "    testing = enclaveCheck(list(testSet),unitNbrs)\n",
    "    print(\"unbroken and noEnclave for proposed modified unit list for HD\",t,\"are\",testing[0],testing[1] )\n",
    "    if testing[0] and testing[1] and testPop < maxDistrictPop:\n",
    "        print(enclaveCheck(list(testSet),unitNbrs)[0:2])\n",
    "        print(\"it is a go.  modify unit list and pop\")\n",
    "        HDunitList[t] = list(testSet)\n",
    "        HDvPop[t] = np.sum([unitPop[u] for u in HDunitList[t] ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d74f8b1a-6670-4729-8696-5813dfc86aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4186 [967, 551, 1866, 2994, 628]\n"
     ]
    }
   ],
   "source": [
    "for t in stillOver[2:]:\n",
    "    print(t,HDunitList[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cfe35342-6d6e-493c-9697-99fee2c62b41",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'HDnAddedUnits' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[53], line 53\u001b[0m\n\u001b[0;32m     51\u001b[0m HDunitList[t] \u001b[38;5;241m=\u001b[39m contigUs \u001b[38;5;241m+\u001b[39m addedList\n\u001b[0;32m     52\u001b[0m HDvPop[t]    \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum( [unitPop[u] \u001b[38;5;28;01mfor\u001b[39;00m u \u001b[38;5;129;01min\u001b[39;00m HDunitList[t] ] )\n\u001b[1;32m---> 53\u001b[0m \u001b[43mHDnAddedUnits\u001b[49m[t] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(addedList)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'HDnAddedUnits' is not defined"
     ]
    }
   ],
   "source": [
    "for t in stillOver[2:]:  #do a simplified MUST SPAWN\n",
    "    maxGap = maxDistrictPop - aDP\n",
    "    starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    currPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    contigPop = currPop\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = list()\n",
    "    for uu in adjoiners:\n",
    "        testing = enclaveCheck(currList + [uu],unitNbrs)\n",
    "        if testing[0] and testing[1] and currPop + unitPop[uu] < maxDistrictPop:\n",
    "            nearHDlist.append(uu)\n",
    "    nearHDscore = [HDCP[t].distance(unitCP[uu]) for uu in nearHDlist ]  #simplified\n",
    "    stillGoing = True\n",
    "    \n",
    "    while currPop < minDistrictPop and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            currPop += unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] + currPop < maxDistrictPop:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append(HDCP[t].distance(unitCP[uu]) )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] + currPop > maxDistrictPop:\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "879a986a-f3f4-4194-90e0-cfecdee18108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4186 [551, 967, 2461, 1254, 2187, 749, 880, 1286, 2994, 1866, 2945, 552, 1229, 971, 800, 1838, 2584, 1848, 147] 0.9644393534341607\n"
     ]
    }
   ],
   "source": [
    "for t in stillOver[2:]:\n",
    "    print(t,HDunitList[t], HDvPop[t]/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "158ffada-288a-4574-8180-24f7c68cacd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 0 underpopped HDs and 0 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "minDistrictPop, maxDistrictPop = 0.95*aDP, 1.05*aDP\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71ce627d-1292-4e17-b25f-08d2ecb7604f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "ee6cfb91-95f5-4ffd-8017-3673727dae61",
   "metadata": {},
   "source": [
    "print(\"here are centerpoints of overpopped HDs\")\n",
    "for t in stillOver:\n",
    "    plotPoly(HDCP[t].buffer(0.02),1)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.2 :\n",
    "        plotPoly(unitGeom[u],0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dc736ef8-0f54-4330-918c-e287db517f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(homeU[t],\"unit is not in HD\",t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "348895e0-eef9-4b4d-a085-85a15daa824a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing to add some splits if we choose to do so\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "0a2a4e59-8793-4512-b75d-3967cbb8d933",
   "metadata": {},
   "source": [
    "print(\"preparing to create some splits if we choose to do so\") #see OH_1850COIsnap15 for this ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04872c1c-968b-49dd-9086-2592b6a7950b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1ee7c8e4-cb8f-4cec-97ca-cf36c605fe52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, looking good.  Update the HDvPops to reflect the partials, check contiguity one last time\n",
      "final check on HD 0\n",
      "final check on HD 1000\n",
      "final check on HD 2000\n",
      "final check on HD 3000\n",
      "final check on HD 4000\n",
      "final check on HD 5000\n",
      "final check on HD 6000\n",
      "final check on HD 7000\n",
      "final check on HD 8000\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, looking good.  Update the HDvPops to reflect the partials, check contiguity one last time\")\n",
    "HDvPop = [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if t%1000 == 0:\n",
    "        print(\"final check on HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if splitUnitNo[t] >= 0:\n",
    "        u = splitUnitNo[t]\n",
    "        if u in HDunitList[t]:  #we had added the full unit pop (shouldn't have happened)\n",
    "            HDvPop[t] -= (1. - splitUnitFrac[t]) * unitPop[u]\n",
    "        else:\n",
    "            HDvPop[t] += splitUnitFrac[t] * unitPop[u]\n",
    "            HDunitList[t].append(splitUnitNo[t])\n",
    "    \n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4)\n",
    "    if not (unbroken and noEnclave and abs(HDvPop[t] - aDP) < 0.05*aDP ):\n",
    "        print(\"ERROR for HD\",t,unbroken,noEnclave,HDvPop[t]/aDP,\"unbroken,noEnclave,HDvPop/aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8fad08be-7da6-43c0-9223-26a53ac7c6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save these unpatched results to temp file\n"
     ]
    }
   ],
   "source": [
    "print(\"save these unpatched results to temp file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"COI1850goodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9c2c48bf-55cc-4388-ae56-7c27108f5c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optional restart.  Read in unpatched\n",
      "I read in a total of 8941 home district unit lists, of which 8934 are populated\n"
     ]
    }
   ],
   "source": [
    "############ OPTIONAL RESTART *******\n",
    "print(\"optional restart.  Read in unpatched\")\n",
    "inDF = pd.read_csv(\"./2024state_HD_output/OH8941_99COI1850goodPop.csv\")\n",
    "HDweight, homeU, homeC = inDF[\"HDweight\"],inDF[\"homeU\"],inDF[\"homeC\"]\n",
    "splitUnitNo, splitUnitFrac = inDF[\"splitUnitNo\"],inDF[\"splitUnitFrac\"]\n",
    "nHDs = len(HDweight)\n",
    "popHDlist = list()\n",
    "for h in range(nHDs):\n",
    "    if HDweight[h] > 0:\n",
    "        popHDlist.append(h)\n",
    "unitString = inDF[\"HDunitList\"]\n",
    "HDunitList = [ast.literal_eval(unitString[h]) for h in range(nHDs)]\n",
    "print(\"I read in a total of\",nHDs,\"home district unit lists, of which\",len(popHDlist),\"are populated\")\n",
    "HDvPop = inDF[\"HDvPop\"].to_numpy()\n",
    "HDCPx, HDCPy = inDF[\"centroid x\"],inDF[\"centroid y\"]\n",
    "HDCP = [Point(HDCPx[h],HDCPy[h]) for h in range(nHDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9436e8b9-e204-41c0-96d3-b5eef5b118c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current avg use and its sd are 1.02052 0.45008\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"updating our unitUse\")\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "    if splitUnitNo[t] >= 0 :\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "78d8647b-5421-4a27-889f-27315e9e7695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and our usage vs. pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"... and our usage vs. pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "prepatchedUnitUse = unitUse.copy() #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0dd75303-2a1a-4d1d-a9ad-6962da74e2cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will freeze (avoid patching) 0 HDs with a split unit\n"
     ]
    }
   ],
   "source": [
    "hasSplitUnit = list()\n",
    "for h in range(nHDs):\n",
    "    if splitUnitNo[h] >= 0 :\n",
    "        hasSplitUnit.append(h)\n",
    "print(\"we will freeze (avoid patching)\",len(hasSplitUnit),\"HDs with a split unit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "49a4f6d9-845b-4bcb-a4d3-433fe780ee99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 1502\n",
      "working on avg unit-to-HDcp dist for HD 3003\n",
      "working on avg unit-to-HDcp dist for HD 4505\n",
      "working on avg unit-to-HDcp dist for HD 6006\n",
      "working on avg unit-to-HDcp dist for HD 7506\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([]) #for basic muni snap, there are no special corner units to freeze\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1500 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(HDCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "820f5f1e-f449-4cfb-94fc-96d1aab07674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "hdCP = HDCP.copy() #nomenclature\n",
    "print(minDistrictPop/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e5bba0ce-dc4a-494e-86b3-0fbefed0e032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For now, we will disallow (freeze) HDs from patching.\n",
      "Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 2369 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 2493 units out of 3438 with usage below 0.98\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "Let's further tighten the distro with exchanges up to 29796 = aDP* 0.25\n",
      "current avg and SD of unit usage are 1.02043 0.45015 . Now trying to increase up to 300 units' underusage\n",
      "all done trying2increase usage of unit 3426 final usage = 0.32348 38 sec elapsed 25 61 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.02046 0.44905\n",
      "all done trying2increase usage of unit 3156 final usage = 0.98479 44 sec elapsed 85 126 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.0204 0.44645\n",
      "all done trying2increase usage of unit 3014 final usage = 0.41472 127 sec elapsed 24 133 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 1.02039 0.44512\n",
      "all done trying2increase usage of unit 2375 final usage = 0.88157 154 sec elapsed 61 105 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.02045 0.44192\n",
      "all done trying2increase usage of unit 2676 final usage = 0.63156 166 sec elapsed 43 131 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02042 0.43965\n",
      "all done trying2increase usage of unit 893 final usage = 0.99809 172 sec elapsed 58 30 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02036 0.43635\n",
      "all done trying2increase usage of unit 223 final usage = 0.73281 189 sec elapsed 52 7 complete, failed patches, unstarted= 291\n",
      "current avg and SD of usage are 1.02036 0.43398\n",
      "all done trying2increase usage of unit 2809 final usage = 0.90642 210 sec elapsed 67 51 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02027 0.43176\n",
      "all done trying2increase usage of unit 3420 final usage = 0.50234 238 sec elapsed 31 25 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02027 0.43042\n",
      "begin usage increase for unit 1279 with usage 0.19373 . Sec, total UU units tried = 238 10\n",
      "all done trying2increase usage of unit 1279 final usage = 0.78305 260 sec elapsed 51 92 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02039 0.42907\n",
      "all done trying2increase usage of unit 2167 final usage = 0.41684 324 sec elapsed 19 138 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02037 0.42819\n",
      "all done trying2increase usage of unit 748 final usage = 0.99044 340 sec elapsed 73 2 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.02025 0.42498\n",
      "all done trying2increase usage of unit 1398 final usage = 0.99374 355 sec elapsed 79 74 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.02031 0.42342\n",
      "all done trying2increase usage of unit 2263 final usage = 0.98387 371 sec elapsed 70 14 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02021 0.42133\n",
      "all done trying2increase usage of unit 1774 final usage = 0.98077 381 sec elapsed 69 54 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02032 0.41825\n",
      "all done trying2increase usage of unit 2635 final usage = 0.98543 394 sec elapsed 66 7 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.02043 0.41418\n",
      "all done trying2increase usage of unit 1122 final usage = 0.98166 407 sec elapsed 61 8 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.0205 0.40942\n",
      "all done trying2increase usage of unit 3166 final usage = 0.84677 422 sec elapsed 46 103 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02053 0.40662\n",
      "all done trying2increase usage of unit 193 final usage = 0.98203 439 sec elapsed 67 8 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.02051 0.40437\n",
      "begin usage increase for unit 760 with usage 0.23366 . Sec, total UU units tried = 439 20\n",
      "all done trying2increase usage of unit 760 final usage = 0.91078 445 sec elapsed 71 27 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.02037 0.40195\n",
      "all done trying2increase usage of unit 1333 final usage = 0.98547 453 sec elapsed 62 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02035 0.39886\n",
      "all done trying2increase usage of unit 862 final usage = 0.98307 461 sec elapsed 57 39 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02028 0.39575\n",
      "all done trying2increase usage of unit 635 final usage = 0.60065 471 sec elapsed 31 105 complete, failed patches, unstarted= 66\n",
      "current avg and SD of usage are 1.02029 0.39324\n",
      "all done trying2increase usage of unit 1973 final usage = 0.89093 539 sec elapsed 64 75 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02019 0.39107\n",
      "all done trying2increase usage of unit 3063 final usage = 0.87461 542 sec elapsed 45 12 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02017 0.38881\n",
      "all done trying2increase usage of unit 3430 final usage = 0.81411 572 sec elapsed 56 64 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.02016 0.38699\n",
      "all done trying2increase usage of unit 2173 final usage = 0.98611 576 sec elapsed 63 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02018 0.38445\n",
      "all done trying2increase usage of unit 1060 final usage = 0.91862 609 sec elapsed 74 0 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.02018 0.38079\n",
      "all done trying2increase usage of unit 761 final usage = 0.98412 695 sec elapsed 76 61 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02018 0.3786\n",
      "begin usage increase for unit 3376 with usage 0.27671 . Sec, total UU units tried = 695 30\n",
      "all done trying2increase usage of unit 3376 final usage = 0.69186 719 sec elapsed 35 88 complete, failed patches, unstarted= 40\n",
      "current avg and SD of usage are 1.02025 0.37769\n",
      "all done trying2increase usage of unit 2414 final usage = 0.9858 727 sec elapsed 53 50 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02007 0.37245\n",
      "all done trying2increase usage of unit 2981 final usage = 0.98842 738 sec elapsed 60 2 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.0201 0.3702\n",
      "all done trying2increase usage of unit 1389 final usage = 0.9814 750 sec elapsed 59 21 complete, failed patches, unstarted= 94\n",
      "current avg and SD of usage are 1.02003 0.36774\n",
      "all done trying2increase usage of unit 1614 final usage = 0.74534 769 sec elapsed 43 79 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02008 0.3654\n",
      "all done trying2increase usage of unit 3366 final usage = 0.49012 788 sec elapsed 16 70 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02014 0.36493\n",
      "all done trying2increase usage of unit 2037 final usage = 0.55551 795 sec elapsed 26 131 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02014 0.36405\n",
      "all done trying2increase usage of unit 1110 final usage = 0.98488 835 sec elapsed 56 28 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02012 0.36234\n",
      "all done trying2increase usage of unit 1482 final usage = 0.6151 853 sec elapsed 27 31 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.02013 0.36037\n",
      "all done trying2increase usage of unit 2863 final usage = 0.82747 887 sec elapsed 50 185 complete, failed patches, unstarted= 38\n",
      "current avg and SD of usage are 1.02016 0.35908\n",
      "begin usage increase for unit 2514 with usage 0.31651 . Sec, total UU units tried = 887 40\n",
      "all done trying2increase usage of unit 2514 final usage = 0.90369 912 sec elapsed 49 88 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02026 0.35738\n",
      "all done trying2increase usage of unit 1996 final usage = 0.4806 914 sec elapsed 13 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02026 0.35695\n",
      "all done trying2increase usage of unit 2097 final usage = 0.37441 924 sec elapsed 4 72 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.02028 0.35678\n",
      "all done trying2increase usage of unit 2680 final usage = 0.94945 955 sec elapsed 60 158 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.02038 0.35429\n",
      "all done trying2increase usage of unit 2359 final usage = 0.3718 961 sec elapsed 5 19 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02042 0.35417\n",
      "all done trying2increase usage of unit 2516 final usage = 0.9962 965 sec elapsed 55 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02036 0.3526\n",
      "all done trying2increase usage of unit 2609 final usage = 0.49132 977 sec elapsed 13 91 complete, failed patches, unstarted= 66\n",
      "current avg and SD of usage are 1.02043 0.35218\n",
      "all done trying2increase usage of unit 253 final usage = 0.98109 993 sec elapsed 56 19 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02043 0.35011\n",
      "all done trying2increase usage of unit 3000 final usage = 0.99105 1002 sec elapsed 56 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02032 0.34771\n",
      "all done trying2increase usage of unit 2259 final usage = 0.98979 1005 sec elapsed 53 18 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02012 0.34596\n",
      "begin usage increase for unit 877 with usage 0.35397 . Sec, total UU units tried = 1005 50\n",
      "all done trying2increase usage of unit 877 final usage = 0.98454 1020 sec elapsed 63 6 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.02011 0.34446\n",
      "all done trying2increase usage of unit 1770 final usage = 0.98662 1049 sec elapsed 52 91 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.02015 0.34169\n",
      "all done trying2increase usage of unit 1209 final usage = 0.41008 1094 sec elapsed 5 104 complete, failed patches, unstarted= 41\n",
      "current avg and SD of usage are 1.02016 0.34151\n",
      "all done trying2increase usage of unit 2571 final usage = 0.4235 1127 sec elapsed 6 72 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02015 0.34136\n",
      "all done trying2increase usage of unit 2994 final usage = 0.90592 1132 sec elapsed 45 63 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02017 0.34066\n",
      "all done trying2increase usage of unit 122 final usage = 0.54227 1153 sec elapsed 16 150 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02017 0.34004\n",
      "all done trying2increase usage of unit 1894 final usage = 0.81375 1176 sec elapsed 46 74 complete, failed patches, unstarted= 33\n",
      "current avg and SD of usage are 1.02015 0.33847\n",
      "all done trying2increase usage of unit 3140 final usage = 0.46853 1187 sec elapsed 10 76 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02018 0.33826\n",
      "all done trying2increase usage of unit 854 final usage = 0.54924 1202 sec elapsed 17 94 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.0202 0.33777\n",
      "all done trying2increase usage of unit 2962 final usage = 0.9802 1215 sec elapsed 49 28 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02013 0.33473\n",
      "begin usage increase for unit 2285 with usage 0.3718 . Sec, total UU units tried = 1215 60\n",
      "all done trying2increase usage of unit 2285 final usage = 0.44442 1221 sec elapsed 8 22 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02016 0.33456\n",
      "all done trying2increase usage of unit 796 final usage = 0.95064 1235 sec elapsed 50 87 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02012 0.33302\n",
      "all done trying2increase usage of unit 1082 final usage = 0.98188 1247 sec elapsed 50 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02009 0.33109\n",
      "all done trying2increase usage of unit 1914 final usage = 0.8398 1265 sec elapsed 42 50 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02015 0.33017\n",
      "all done trying2increase usage of unit 2541 final usage = 0.98614 1271 sec elapsed 50 1 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.02017 0.32854\n",
      "all done trying2increase usage of unit 1939 final usage = 0.46036 1302 sec elapsed 9 80 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.02018 0.32841\n",
      "all done trying2increase usage of unit 2614 final usage = 0.98197 1335 sec elapsed 48 89 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02011 0.32762\n",
      "all done trying2increase usage of unit 600 final usage = 0.99234 1367 sec elapsed 49 10 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.02007 0.32528\n",
      "all done trying2increase usage of unit 1587 final usage = 0.65564 1409 sec elapsed 23 122 complete, failed patches, unstarted= 57\n",
      "current avg and SD of usage are 1.02011 0.32485\n",
      "all done trying2increase usage of unit 2774 final usage = 0.98517 1416 sec elapsed 53 5 complete, failed patches, unstarted= 36\n",
      "current avg and SD of usage are 1.02006 0.32268\n",
      "begin usage increase for unit 349 with usage 0.38922 . Sec, total UU units tried = 1416 70\n",
      "all done trying2increase usage of unit 349 final usage = 0.67035 1424 sec elapsed 23 56 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02007 0.32214\n",
      "all done trying2increase usage of unit 3134 final usage = 0.47296 1434 sec elapsed 4 94 complete, failed patches, unstarted= 63\n",
      "current avg and SD of usage are 1.02002 0.32173\n",
      "all done trying2increase usage of unit 2654 final usage = 0.66175 1491 sec elapsed 24 233 complete, failed patches, unstarted= 81\n",
      "current avg and SD of usage are 1.02001 0.32091\n",
      "all done trying2increase usage of unit 838 final usage = 0.98516 1501 sec elapsed 70 1 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02004 0.31864\n",
      "all done trying2increase usage of unit 2139 final usage = 0.81604 1518 sec elapsed 38 70 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02002 0.31773\n",
      "all done trying2increase usage of unit 190 final usage = 0.54498 1533 sec elapsed 13 46 complete, failed patches, unstarted= 50\n",
      "current avg and SD of usage are 1.02002 0.31752\n",
      "all done trying2increase usage of unit 3316 final usage = 0.5161 1567 sec elapsed 9 27 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.02003 0.31701\n",
      "all done trying2increase usage of unit 920 final usage = 0.45755 1577 sec elapsed 5 145 complete, failed patches, unstarted= 62\n",
      "current avg and SD of usage are 1.02004 0.31677\n",
      "all done trying2increase usage of unit 3353 final usage = 0.6624 1602 sec elapsed 25 95 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.0201 0.3158\n",
      "all done trying2increase usage of unit 450 final usage = 0.44548 1648 sec elapsed 5 162 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.0201 0.31568\n",
      "begin usage increase for unit 498 with usage 0.40245 . Sec, total UU units tried = 1648 80\n",
      "all done trying2increase usage of unit 498 final usage = 0.47396 1657 sec elapsed 16 6 complete, failed patches, unstarted= 128\n",
      "current avg and SD of usage are 1.02012 0.31546\n",
      "all done trying2increase usage of unit 1895 final usage = 0.72649 1676 sec elapsed 29 109 complete, failed patches, unstarted= 33\n",
      "current avg and SD of usage are 1.02017 0.3146\n",
      "all done trying2increase usage of unit 2015 final usage = 0.53957 1700 sec elapsed 10 220 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.02018 0.31438\n",
      "all done trying2increase usage of unit 2943 final usage = 0.98962 1707 sec elapsed 48 8 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02023 0.31398\n",
      "all done trying2increase usage of unit 3435 final usage = 0.44565 1725 sec elapsed 4 76 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.02023 0.31391\n",
      "all done trying2increase usage of unit 730 final usage = 0.54231 1758 sec elapsed 16 103 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02027 0.31362\n",
      "all done trying2increase usage of unit 1789 final usage = 0.95141 1797 sec elapsed 60 3 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.02034 0.31268\n",
      "all done trying2increase usage of unit 2463 final usage = 0.98826 1803 sec elapsed 43 8 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.02033 0.31181\n",
      "all done trying2increase usage of unit 2655 final usage = 0.985 1808 sec elapsed 53 3 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02027 0.31021\n",
      "all done trying2increase usage of unit 3388 final usage = 0.47667 1840 sec elapsed 4 39 complete, failed patches, unstarted= 86\n",
      "current avg and SD of usage are 1.02029 0.30998\n",
      "begin usage increase for unit 2531 with usage 0.41633 . Sec, total UU units tried = 1840 90\n",
      "all done trying2increase usage of unit 2531 final usage = 0.99189 1850 sec elapsed 51 8 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.02025 0.3087\n",
      "all done trying2increase usage of unit 200 final usage = 0.49978 1865 sec elapsed 9 21 complete, failed patches, unstarted= 82\n",
      "current avg and SD of usage are 1.02025 0.30852\n",
      "all done trying2increase usage of unit 352 final usage = 0.89655 1887 sec elapsed 37 104 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.02024 0.30765\n",
      "all done trying2increase usage of unit 633 final usage = 0.99143 1901 sec elapsed 47 126 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02017 0.30516\n",
      "all done trying2increase usage of unit 1349 final usage = 0.48591 1933 sec elapsed 5 137 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02016 0.30497\n",
      "all done trying2increase usage of unit 3313 final usage = 0.52837 1953 sec elapsed 10 19 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02017 0.3046\n",
      "all done trying2increase usage of unit 1867 final usage = 0.49637 1983 sec elapsed 8 79 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.02019 0.30447\n",
      "all done trying2increase usage of unit 3126 final usage = 0.98608 2010 sec elapsed 37 10 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02028 0.30362\n",
      "all done trying2increase usage of unit 3365 final usage = 0.45592 2027 sec elapsed 2 43 complete, failed patches, unstarted= 112\n",
      "current avg and SD of usage are 1.02028 0.30357\n",
      "all done trying2increase usage of unit 3154 final usage = 0.98195 2036 sec elapsed 49 107 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.02029 0.30218\n",
      "begin usage increase for unit 3431 with usage 0.43685 . Sec, total UU units tried = 2036 100\n",
      "all done trying2increase usage of unit 3431 final usage = 0.43685 2060 sec elapsed 0 87 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02029 0.30218\n",
      "all done trying2increase usage of unit 1351 final usage = 0.47525 2088 sec elapsed 3 87 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.0203 0.30205\n",
      "all done trying2increase usage of unit 927 final usage = 0.92105 2099 sec elapsed 41 130 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02041 0.30104\n",
      "all done trying2increase usage of unit 3363 final usage = 0.44026 2139 sec elapsed 0 127 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02041 0.30104\n",
      "all done trying2increase usage of unit 1718 final usage = 0.60443 2189 sec elapsed 14 189 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02046 0.30062\n",
      "all done trying2increase usage of unit 104 final usage = 0.56027 2257 sec elapsed 13 138 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02047 0.30038\n",
      "all done trying2increase usage of unit 2493 final usage = 0.68611 2275 sec elapsed 23 83 complete, failed patches, unstarted= 29\n",
      "current avg and SD of usage are 1.02049 0.29956\n",
      "all done trying2increase usage of unit 2364 final usage = 0.44442 2279 sec elapsed 0 27 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02049 0.29956\n",
      "all done trying2increase usage of unit 2357 final usage = 0.45818 2283 sec elapsed 2 6 complete, failed patches, unstarted= 85\n",
      "current avg and SD of usage are 1.0205 0.29953\n",
      "all done trying2increase usage of unit 1953 final usage = 0.52389 2331 sec elapsed 7 134 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.02052 0.29944\n",
      "begin usage increase for unit 3362 with usage 0.44912 . Sec, total UU units tried = 2331 110\n",
      "all done trying2increase usage of unit 3362 final usage = 0.44912 2346 sec elapsed 0 56 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02052 0.29944\n",
      "all done trying2increase usage of unit 1571 final usage = 0.4502 2360 sec elapsed 0 79 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.02052 0.29944\n",
      "all done trying2increase usage of unit 1150 final usage = 0.99157 2378 sec elapsed 48 41 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.02057 0.29821\n",
      "all done trying2increase usage of unit 1506 final usage = 0.45416 2381 sec elapsed 0 11 complete, failed patches, unstarted= 40\n",
      "current avg and SD of usage are 1.02057 0.29821\n",
      "all done trying2increase usage of unit 2652 final usage = 0.98126 2384 sec elapsed 59 2 complete, failed patches, unstarted= 79\n",
      "current avg and SD of usage are 1.02057 0.29678\n",
      "all done trying2increase usage of unit 3425 final usage = 0.52684 2413 sec elapsed 6 107 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02058 0.29662\n",
      "all done trying2increase usage of unit 991 final usage = 0.45915 2429 sec elapsed 0 68 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.02058 0.29662\n",
      "all done trying2increase usage of unit 1355 final usage = 0.94398 2563 sec elapsed 52 52 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.02062 0.29579\n",
      "all done trying2increase usage of unit 557 final usage = 0.46541 2613 sec elapsed 1 161 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.02062 0.29578\n",
      "all done trying2increase usage of unit 946 final usage = 0.46336 2614 sec elapsed 0 0 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.02062 0.29578\n",
      "begin usage increase for unit 1512 with usage 0.46336 . Sec, total UU units tried = 2614 120\n",
      "all done trying2increase usage of unit 1512 final usage = 0.47318 2615 sec elapsed 1 8 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02062 0.29576\n",
      "all done trying2increase usage of unit 2588 final usage = 0.46336 2618 sec elapsed 0 9 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.02062 0.29576\n",
      "all done trying2increase usage of unit 2591 final usage = 0.46336 2621 sec elapsed 0 9 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.02062 0.29576\n",
      "all done trying2increase usage of unit 3358 final usage = 0.49185 2636 sec elapsed 2 85 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02063 0.29571\n",
      "all done trying2increase usage of unit 1523 final usage = 0.46568 2639 sec elapsed 0 20 complete, failed patches, unstarted= 42\n",
      "current avg and SD of usage are 1.02063 0.29571\n",
      "all done trying2increase usage of unit 3386 final usage = 0.5107 2663 sec elapsed 3 41 complete, failed patches, unstarted= 85\n",
      "current avg and SD of usage are 1.02065 0.29562\n",
      "all done trying2increase usage of unit 1641 final usage = 0.59601 2685 sec elapsed 10 140 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02067 0.29552\n",
      "all done trying2increase usage of unit 2043 final usage = 0.98444 2689 sec elapsed 58 73 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02066 0.29447\n",
      "all done trying2increase usage of unit 2343 final usage = 0.46989 2695 sec elapsed 1 10 complete, failed patches, unstarted= 134\n",
      "current avg and SD of usage are 1.02066 0.29446\n",
      "all done trying2increase usage of unit 1328 final usage = 0.98187 2701 sec elapsed 45 49 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02067 0.29353\n",
      "begin usage increase for unit 2908 with usage 0.47109 . Sec, total UU units tried = 2701 130\n",
      "all done trying2increase usage of unit 2908 final usage = 0.75661 2716 sec elapsed 26 85 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0207 0.29309\n",
      "all done trying2increase usage of unit 2265 final usage = 0.9925 2720 sec elapsed 41 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02078 0.29213\n",
      "all done trying2increase usage of unit 1594 final usage = 0.5733 2761 sec elapsed 14 166 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02081 0.2917\n",
      "all done trying2increase usage of unit 329 final usage = 0.98882 2765 sec elapsed 44 3 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02081 0.29082\n",
      "all done trying2increase usage of unit 1513 final usage = 0.47318 2767 sec elapsed 0 8 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02081 0.29082\n",
      "all done trying2increase usage of unit 1514 final usage = 0.47318 2769 sec elapsed 0 8 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02081 0.29082\n",
      "all done trying2increase usage of unit 3168 final usage = 0.64076 2782 sec elapsed 15 133 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02082 0.29068\n",
      "all done trying2increase usage of unit 523 final usage = 0.99335 2791 sec elapsed 43 11 complete, failed patches, unstarted= 157\n",
      "current avg and SD of usage are 1.02082 0.28811\n",
      "all done trying2increase usage of unit 479 final usage = 0.48645 2795 sec elapsed 1 30 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02082 0.28809\n",
      "all done trying2increase usage of unit 2777 final usage = 0.9936 2803 sec elapsed 49 11 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.0209 0.28673\n",
      "begin usage increase for unit 2355 with usage 0.48307 . Sec, total UU units tried = 2803 140\n",
      "all done trying2increase usage of unit 2355 final usage = 0.49279 2810 sec elapsed 2 13 complete, failed patches, unstarted= 102\n",
      "current avg and SD of usage are 1.0209 0.28671\n",
      "all done trying2increase usage of unit 2663 final usage = 0.9902 2814 sec elapsed 38 6 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02089 0.28556\n",
      "all done trying2increase usage of unit 89 final usage = 0.98677 2821 sec elapsed 43 7 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02085 0.28406\n",
      "all done trying2increase usage of unit 2168 final usage = 0.48591 2858 sec elapsed 0 138 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02085 0.28406\n",
      "all done trying2increase usage of unit 3198 final usage = 0.60065 2952 sec elapsed 12 154 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02086 0.28369\n",
      "all done trying2increase usage of unit 3383 final usage = 0.5066 2985 sec elapsed 2 123 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02086 0.2836\n",
      "all done trying2increase usage of unit 1575 final usage = 0.48875 2992 sec elapsed 0 28 complete, failed patches, unstarted= 91\n",
      "current avg and SD of usage are 1.02086 0.2836\n",
      "all done trying2increase usage of unit 3434 final usage = 0.489 2996 sec elapsed 0 7 complete, failed patches, unstarted= 98\n",
      "current avg and SD of usage are 1.02086 0.2836\n",
      "all done trying2increase usage of unit 2606 final usage = 0.55298 3004 sec elapsed 5 184 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02088 0.28333\n",
      "all done trying2increase usage of unit 2408 final usage = 0.50243 3033 sec elapsed 2 71 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02088 0.28332\n",
      "begin usage increase for unit 1956 with usage 0.49026 . Sec, total UU units tried = 3033 150\n",
      "all done trying2increase usage of unit 1956 final usage = 0.53096 3072 sec elapsed 6 110 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.02089 0.28323\n",
      "all done trying2increase usage of unit 1278 final usage = 0.49185 3104 sec elapsed 0 137 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02089 0.28323\n",
      "all done trying2increase usage of unit 2363 final usage = 0.49208 3109 sec elapsed 0 15 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.02089 0.28323\n",
      "all done trying2increase usage of unit 2230 final usage = 0.98182 3119 sec elapsed 46 48 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02083 0.28206\n",
      "all done trying2increase usage of unit 2653 final usage = 0.50219 3125 sec elapsed 1 10 complete, failed patches, unstarted= 86\n",
      "current avg and SD of usage are 1.02084 0.28204\n",
      "all done trying2increase usage of unit 1502 final usage = 0.52428 3133 sec elapsed 4 42 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02085 0.28193\n",
      "all done trying2increase usage of unit 188 final usage = 0.57228 3140 sec elapsed 5 14 complete, failed patches, unstarted= 88\n",
      "current avg and SD of usage are 1.02087 0.28178\n",
      "all done trying2increase usage of unit 80 final usage = 0.54903 3180 sec elapsed 6 128 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02087 0.28168\n",
      "all done trying2increase usage of unit 2547 final usage = 0.98756 3183 sec elapsed 43 3 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02085 0.28095\n",
      "all done trying2increase usage of unit 3432 final usage = 0.53245 3203 sec elapsed 2 83 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.02085 0.2809\n",
      "begin usage increase for unit 969 with usage 0.50518 . Sec, total UU units tried = 3203 160\n",
      "all done trying2increase usage of unit 969 final usage = 0.51448 3269 sec elapsed 1 164 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.02085 0.28089\n",
      "all done trying2increase usage of unit 2077 final usage = 0.98545 3274 sec elapsed 37 26 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.02091 0.2797\n",
      "all done trying2increase usage of unit 1845 final usage = 0.54253 3293 sec elapsed 3 164 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02093 0.27965\n",
      "all done trying2increase usage of unit 1963 final usage = 0.6539 3315 sec elapsed 14 67 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.02096 0.27943\n",
      "all done trying2increase usage of unit 1470 final usage = 0.63529 3317 sec elapsed 14 8 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02095 0.27922\n",
      "all done trying2increase usage of unit 2352 final usage = 0.62364 3345 sec elapsed 11 126 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.02098 0.27895\n",
      "all done trying2increase usage of unit 1498 final usage = 0.54095 3352 sec elapsed 4 23 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.02099 0.27887\n",
      "all done trying2increase usage of unit 2593 final usage = 0.51004 3373 sec elapsed 0 127 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02099 0.27887\n",
      "all done trying2increase usage of unit 1212 final usage = 0.52949 3399 sec elapsed 2 63 complete, failed patches, unstarted= 19\n",
      "current avg and SD of usage are 1.021 0.27884\n",
      "all done trying2increase usage of unit 720 final usage = 0.51132 3410 sec elapsed 0 40 complete, failed patches, unstarted= 73\n",
      "current avg and SD of usage are 1.021 0.27884\n",
      "begin usage increase for unit 1194 with usage 0.51227 . Sec, total UU units tried = 3410 170\n",
      "all done trying2increase usage of unit 1194 final usage = 0.98509 3415 sec elapsed 34 17 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02093 0.27793\n",
      "all done trying2increase usage of unit 356 final usage = 0.64058 3430 sec elapsed 11 60 complete, failed patches, unstarted= 88\n",
      "current avg and SD of usage are 1.02093 0.27764\n",
      "all done trying2increase usage of unit 315 final usage = 0.78419 3443 sec elapsed 22 60 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02095 0.27694\n",
      "all done trying2increase usage of unit 3262 final usage = 0.51903 3504 sec elapsed 1 162 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02095 0.27693\n",
      "all done trying2increase usage of unit 3196 final usage = 0.73868 3574 sec elapsed 20 133 complete, failed patches, unstarted= 78\n",
      "current avg and SD of usage are 1.021 0.27645\n",
      "all done trying2increase usage of unit 2411 final usage = 0.52254 3580 sec elapsed 1 13 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.021 0.27644\n",
      "all done trying2increase usage of unit 871 final usage = 0.98746 3583 sec elapsed 38 4 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.02101 0.27541\n",
      "all done trying2increase usage of unit 2682 final usage = 0.98497 3594 sec elapsed 42 102 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.021 0.27454\n",
      "all done trying2increase usage of unit 858 final usage = 0.5372 3617 sec elapsed 2 135 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.021 0.27446\n",
      "all done trying2increase usage of unit 1491 final usage = 0.52955 3622 sec elapsed 1 23 complete, failed patches, unstarted= 36\n",
      "current avg and SD of usage are 1.021 0.27444\n",
      "begin usage increase for unit 1592 with usage 0.52257 . Sec, total UU units tried = 3622 180\n",
      "all done trying2increase usage of unit 1592 final usage = 0.68347 3642 sec elapsed 9 79 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02098 0.27397\n",
      "all done trying2increase usage of unit 217 final usage = 0.85746 3659 sec elapsed 29 23 complete, failed patches, unstarted= 190\n",
      "current avg and SD of usage are 1.02106 0.27356\n",
      "all done trying2increase usage of unit 3433 final usage = 0.52428 3661 sec elapsed 0 1 complete, failed patches, unstarted= 89\n",
      "current avg and SD of usage are 1.02106 0.27356\n",
      "all done trying2increase usage of unit 1144 final usage = 0.93355 3682 sec elapsed 33 44 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.02109 0.27304\n",
      "all done trying2increase usage of unit 1310 final usage = 0.99277 3689 sec elapsed 36 46 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.02113 0.27256\n",
      "all done trying2increase usage of unit 842 final usage = 0.98533 3692 sec elapsed 35 6 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02112 0.27159\n",
      "all done trying2increase usage of unit 1696 final usage = 0.70383 3704 sec elapsed 16 98 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02114 0.27135\n",
      "all done trying2increase usage of unit 1359 final usage = 0.53539 3707 sec elapsed 1 33 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.02114 0.27133\n",
      "all done trying2increase usage of unit 3325 final usage = 0.54034 3741 sec elapsed 1 31 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02114 0.27132\n",
      "all done trying2increase usage of unit 1507 final usage = 0.52938 3745 sec elapsed 0 14 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.02114 0.27132\n",
      "begin usage increase for unit 2807 with usage 0.52946 . Sec, total UU units tried = 3745 190\n",
      "all done trying2increase usage of unit 2807 final usage = 0.98751 3770 sec elapsed 42 42 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02116 0.27041\n",
      "all done trying2increase usage of unit 3413 final usage = 0.64895 3807 sec elapsed 8 126 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.02114 0.26959\n",
      "all done trying2increase usage of unit 2719 final usage = 0.98275 3818 sec elapsed 42 10 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02118 0.26885\n",
      "all done trying2increase usage of unit 2361 final usage = 0.54742 3836 sec elapsed 2 27 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.02118 0.26882\n",
      "all done trying2increase usage of unit 2135 final usage = 0.65 3847 sec elapsed 9 91 complete, failed patches, unstarted= 52\n",
      "current avg and SD of usage are 1.02122 0.26844\n",
      "all done trying2increase usage of unit 3326 final usage = 0.55803 3878 sec elapsed 2 45 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02123 0.26837\n",
      "all done trying2increase usage of unit 2632 final usage = 0.57537 3882 sec elapsed 3 32 complete, failed patches, unstarted= 132\n",
      "current avg and SD of usage are 1.02123 0.26815\n",
      "all done trying2increase usage of unit 1495 final usage = 0.53611 3886 sec elapsed 0 27 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.02123 0.26815\n",
      "all done trying2increase usage of unit 1511 final usage = 0.53611 3889 sec elapsed 0 18 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02123 0.26815\n",
      "all done trying2increase usage of unit 1522 final usage = 0.53619 3901 sec elapsed 0 65 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02123 0.26815\n",
      "begin usage increase for unit 1707 with usage 0.5362 . Sec, total UU units tried = 3901 200\n",
      "all done trying2increase usage of unit 1707 final usage = 0.98505 3908 sec elapsed 53 12 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.02121 0.26707\n",
      "all done trying2increase usage of unit 2036 final usage = 0.98734 3913 sec elapsed 38 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02119 0.26614\n",
      "all done trying2increase usage of unit 1402 final usage = 0.82269 3922 sec elapsed 22 62 complete, failed patches, unstarted= 29\n",
      "current avg and SD of usage are 1.0212 0.2655\n",
      "all done trying2increase usage of unit 2621 final usage = 0.99152 3925 sec elapsed 39 0 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02113 0.26478\n",
      "all done trying2increase usage of unit 2278 final usage = 0.54093 3961 sec elapsed 0 79 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.02113 0.26478\n",
      "all done trying2increase usage of unit 1504 final usage = 0.54229 3963 sec elapsed 0 1 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.02113 0.26478\n",
      "all done trying2increase usage of unit 1510 final usage = 0.54229 3966 sec elapsed 0 17 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02113 0.26478\n",
      "all done trying2increase usage of unit 541 final usage = 0.99044 3971 sec elapsed 45 10 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.02115 0.2636\n",
      "all done trying2increase usage of unit 2202 final usage = 0.67243 3983 sec elapsed 10 57 complete, failed patches, unstarted= 102\n",
      "current avg and SD of usage are 1.02117 0.26345\n",
      "all done trying2increase usage of unit 785 final usage = 0.98153 3990 sec elapsed 54 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02116 0.26274\n",
      "begin usage increase for unit 3389 with usage 0.54395 . Sec, total UU units tried = 3990 210\n",
      "all done trying2increase usage of unit 3389 final usage = 0.55928 4010 sec elapsed 1 121 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02116 0.26271\n",
      "all done trying2increase usage of unit 2404 final usage = 0.54601 4028 sec elapsed 0 55 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02116 0.26271\n",
      "all done trying2increase usage of unit 1456 final usage = 0.60518 4030 sec elapsed 6 9 complete, failed patches, unstarted= 53\n",
      "current avg and SD of usage are 1.02117 0.26263\n",
      "all done trying2increase usage of unit 1488 final usage = 0.54951 4034 sec elapsed 0 6 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.02117 0.26263\n",
      "all done trying2increase usage of unit 843 final usage = 0.99386 4040 sec elapsed 37 33 complete, failed patches, unstarted= 40\n",
      "current avg and SD of usage are 1.02121 0.26221\n",
      "all done trying2increase usage of unit 3309 final usage = 0.57468 4071 sec elapsed 2 41 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.02122 0.26213\n",
      "all done trying2increase usage of unit 789 final usage = 0.71367 4072 sec elapsed 20 7 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02127 0.26167\n",
      "all done trying2increase usage of unit 311 final usage = 0.64867 4081 sec elapsed 8 51 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02129 0.26156\n",
      "all done trying2increase usage of unit 1370 final usage = 0.98614 4090 sec elapsed 41 10 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02129 0.26034\n",
      "all done trying2increase usage of unit 1515 final usage = 0.55795 4091 sec elapsed 0 6 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02129 0.26034\n",
      "begin usage increase for unit 1516 with usage 0.55795 . Sec, total UU units tried = 4091 220\n",
      "all done trying2increase usage of unit 1516 final usage = 0.57802 4096 sec elapsed 2 14 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.0213 0.26028\n",
      "all done trying2increase usage of unit 566 final usage = 0.57767 4105 sec elapsed 2 18 complete, failed patches, unstarted= 119\n",
      "current avg and SD of usage are 1.02131 0.26025\n",
      "all done trying2increase usage of unit 2360 final usage = 0.57253 4128 sec elapsed 2 70 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02131 0.26022\n",
      "all done trying2increase usage of unit 2575 final usage = 0.56016 4132 sec elapsed 0 21 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02131 0.26022\n",
      "all done trying2increase usage of unit 1841 final usage = 0.98239 4137 sec elapsed 37 1 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02135 0.25944\n",
      "all done trying2increase usage of unit 3102 final usage = 0.99156 4149 sec elapsed 32 55 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02139 0.25897\n",
      "all done trying2increase usage of unit 2398 final usage = 0.56195 4154 sec elapsed 0 13 complete, failed patches, unstarted= 94\n",
      "current avg and SD of usage are 1.02139 0.25897\n",
      "all done trying2increase usage of unit 1892 final usage = 0.59449 4190 sec elapsed 3 124 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02139 0.25894\n",
      "all done trying2increase usage of unit 3359 final usage = 0.56417 4217 sec elapsed 0 124 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.02139 0.25894\n",
      "all done trying2increase usage of unit 2924 final usage = 0.61887 4232 sec elapsed 5 97 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02138 0.25886\n",
      "begin usage increase for unit 3312 with usage 0.56737 . Sec, total UU units tried = 4232 230\n",
      "all done trying2increase usage of unit 3312 final usage = 0.57092 4247 sec elapsed 1 23 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.02139 0.25885\n",
      "all done trying2increase usage of unit 2978 final usage = 0.57672 4260 sec elapsed 1 26 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.02139 0.25884\n",
      "all done trying2increase usage of unit 3287 final usage = 0.59377 4299 sec elapsed 3 48 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.0214 0.25879\n",
      "all done trying2increase usage of unit 3271 final usage = 0.56851 4343 sec elapsed 0 88 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.0214 0.25879\n",
      "all done trying2increase usage of unit 2451 final usage = 0.57645 4380 sec elapsed 1 128 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.02141 0.25877\n",
      "all done trying2increase usage of unit 3417 final usage = 0.63858 4416 sec elapsed 7 143 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.02142 0.25871\n",
      "all done trying2increase usage of unit 2372 final usage = 0.78835 4463 sec elapsed 24 191 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.02143 0.2584\n",
      "all done trying2increase usage of unit 3311 final usage = 0.60873 4493 sec elapsed 4 31 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02143 0.25826\n",
      "all done trying2increase usage of unit 1883 final usage = 0.58902 4509 sec elapsed 3 37 complete, failed patches, unstarted= 93\n",
      "current avg and SD of usage are 1.02144 0.25824\n",
      "all done trying2increase usage of unit 1748 final usage = 0.81078 4535 sec elapsed 27 69 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.02145 0.25773\n",
      "begin usage increase for unit 2521 with usage 0.57469 . Sec, total UU units tried = 4535 240\n",
      "all done trying2increase usage of unit 2521 final usage = 0.98302 4540 sec elapsed 40 22 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02148 0.25706\n",
      "all done trying2increase usage of unit 3382 final usage = 0.59961 4574 sec elapsed 2 77 complete, failed patches, unstarted= 235\n",
      "current avg and SD of usage are 1.02148 0.25693\n",
      "all done trying2increase usage of unit 1242 final usage = 0.66051 4633 sec elapsed 6 176 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02147 0.25672\n",
      "all done trying2increase usage of unit 3193 final usage = 0.58419 4657 sec elapsed 1 46 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.02147 0.25669\n",
      "all done trying2increase usage of unit 3112 final usage = 0.57672 4672 sec elapsed 0 35 complete, failed patches, unstarted= 29\n",
      "current avg and SD of usage are 1.02147 0.25669\n",
      "all done trying2increase usage of unit 2905 final usage = 0.99339 4679 sec elapsed 33 3 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02143 0.25603\n",
      "all done trying2increase usage of unit 1448 final usage = 0.90684 4710 sec elapsed 35 94 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02155 0.25558\n",
      "all done trying2increase usage of unit 3315 final usage = 0.5895 4753 sec elapsed 2 33 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02155 0.25557\n",
      "all done trying2increase usage of unit 886 final usage = 0.98471 4761 sec elapsed 37 1 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.0216 0.2551\n",
      "all done trying2increase usage of unit 981 final usage = 0.98435 4768 sec elapsed 27 39 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02159 0.25445\n",
      "begin usage increase for unit 1201 with usage 0.57869 . Sec, total UU units tried = 4768 250\n",
      "all done trying2increase usage of unit 1201 final usage = 0.98608 4773 sec elapsed 29 7 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02155 0.25371\n",
      "all done trying2increase usage of unit 1757 final usage = 0.70884 4786 sec elapsed 14 138 complete, failed patches, unstarted= 54\n",
      "current avg and SD of usage are 1.0216 0.25362\n",
      "all done trying2increase usage of unit 990 final usage = 0.58075 4792 sec elapsed 0 39 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.0216 0.25362\n",
      "all done trying2increase usage of unit 1273 final usage = 0.6589 4813 sec elapsed 6 171 complete, failed patches, unstarted= 41\n",
      "current avg and SD of usage are 1.02158 0.25337\n",
      "all done trying2increase usage of unit 3131 final usage = 0.94424 4817 sec elapsed 30 66 complete, failed patches, unstarted= 94\n",
      "current avg and SD of usage are 1.02174 0.25271\n",
      "all done trying2increase usage of unit 637 final usage = 0.58494 4844 sec elapsed 0 71 complete, failed patches, unstarted= 42\n",
      "current avg and SD of usage are 1.02174 0.25271\n",
      "all done trying2increase usage of unit 756 final usage = 0.68265 4898 sec elapsed 9 139 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02174 0.2525\n",
      "all done trying2increase usage of unit 540 final usage = 0.71545 4911 sec elapsed 11 96 complete, failed patches, unstarted= 67\n",
      "current avg and SD of usage are 1.02176 0.25221\n",
      "all done trying2increase usage of unit 1518 final usage = 0.60076 4924 sec elapsed 1 58 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02177 0.25216\n",
      "all done trying2increase usage of unit 2647 final usage = 0.9861 4931 sec elapsed 29 67 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02182 0.25151\n",
      "begin usage increase for unit 2124 with usage 0.59123 . Sec, total UU units tried = 4931 260\n",
      "all done trying2increase usage of unit 2124 final usage = 0.59123 4943 sec elapsed 0 77 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.02182 0.25151\n",
      "all done trying2increase usage of unit 988 final usage = 0.59134 4959 sec elapsed 0 128 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02182 0.25151\n",
      "all done trying2increase usage of unit 1948 final usage = 0.88041 4976 sec elapsed 24 63 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02186 0.25096\n",
      "all done trying2increase usage of unit 3308 final usage = 0.61727 5000 sec elapsed 3 31 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02186 0.25091\n",
      "all done trying2increase usage of unit 924 final usage = 0.98967 5004 sec elapsed 28 10 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.02185 0.25049\n",
      "all done trying2increase usage of unit 2293 final usage = 0.67697 5060 sec elapsed 9 254 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.02187 0.25038\n",
      "all done trying2increase usage of unit 2574 final usage = 0.9899 5065 sec elapsed 35 10 complete, failed patches, unstarted= 94\n",
      "current avg and SD of usage are 1.02184 0.24964\n",
      "all done trying2increase usage of unit 2407 final usage = 0.59336 5088 sec elapsed 0 63 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.02184 0.24964\n",
      "all done trying2increase usage of unit 2335 final usage = 0.62007 5126 sec elapsed 3 83 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02184 0.24962\n",
      "all done trying2increase usage of unit 1612 final usage = 0.68437 5151 sec elapsed 7 121 complete, failed patches, unstarted= 52\n",
      "current avg and SD of usage are 1.02186 0.24959\n",
      "begin usage increase for unit 3368 with usage 0.59664 . Sec, total UU units tried = 5151 270\n",
      "all done trying2increase usage of unit 3368 final usage = 0.59664 5198 sec elapsed 0 263 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.02186 0.24959\n",
      "all done trying2increase usage of unit 2231 final usage = 0.98438 5202 sec elapsed 36 54 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.02189 0.2492\n",
      "all done trying2increase usage of unit 2287 final usage = 0.5969 5232 sec elapsed 0 147 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.02189 0.2492\n",
      "all done trying2increase usage of unit 1781 final usage = 0.685 5246 sec elapsed 6 123 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.0219 0.2491\n",
      "all done trying2increase usage of unit 2249 final usage = 0.98938 5249 sec elapsed 30 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02193 0.24836\n",
      "all done trying2increase usage of unit 2166 final usage = 0.60076 5257 sec elapsed 0 37 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.02193 0.24836\n",
      "all done trying2increase usage of unit 3357 final usage = 0.60694 5281 sec elapsed 1 169 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.02193 0.24835\n",
      "all done trying2increase usage of unit 3159 final usage = 0.65082 5284 sec elapsed 5 13 complete, failed patches, unstarted= 186\n",
      "current avg and SD of usage are 1.02193 0.24834\n",
      "all done trying2increase usage of unit 52 final usage = 0.98668 5291 sec elapsed 42 9 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.02198 0.2479\n",
      "all done trying2increase usage of unit 2133 final usage = 0.60324 5292 sec elapsed 0 2 complete, failed patches, unstarted= 33\n",
      "current avg and SD of usage are 1.02198 0.2479\n",
      "begin usage increase for unit 2365 with usage 0.60324 . Sec, total UU units tried = 5293 280\n",
      "all done trying2increase usage of unit 2365 final usage = 0.60324 5301 sec elapsed 0 16 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.02198 0.2479\n",
      "all done trying2increase usage of unit 1716 final usage = 0.6054 5334 sec elapsed 0 92 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.02198 0.2479\n",
      "all done trying2increase usage of unit 3002 final usage = 0.98199 5345 sec elapsed 27 29 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.02206 0.24734\n",
      "all done trying2increase usage of unit 95 final usage = 0.69646 5412 sec elapsed 9 141 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.02207 0.24723\n",
      "all done trying2increase usage of unit 1901 final usage = 0.65938 5464 sec elapsed 6 96 complete, failed patches, unstarted= 286\n",
      "current avg and SD of usage are 1.02207 0.24719\n",
      "all done trying2increase usage of unit 3320 final usage = 0.6064 5502 sec elapsed 0 35 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.02207 0.24719\n",
      "all done trying2increase usage of unit 2318 final usage = 0.60746 5536 sec elapsed 0 104 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.02207 0.24719\n",
      "all done trying2increase usage of unit 3153 final usage = 0.60806 5548 sec elapsed 0 25 complete, failed patches, unstarted= 75\n",
      "current avg and SD of usage are 1.02207 0.24719\n",
      "all done trying2increase usage of unit 3370 final usage = 0.62601 5593 sec elapsed 2 162 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.02208 0.24717\n",
      "all done trying2increase usage of unit 2022 final usage = 0.85803 5596 sec elapsed 21 15 complete, failed patches, unstarted= 116\n",
      "current avg and SD of usage are 1.02213 0.24683\n",
      "begin usage increase for unit 3235 with usage 0.60973 . Sec, total UU units tried = 5596 290\n",
      "all done trying2increase usage of unit 3235 final usage = 0.60973 5609 sec elapsed 0 57 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.02213 0.24683\n",
      "all done trying2increase usage of unit 3324 final usage = 0.61191 5636 sec elapsed 0 37 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.02213 0.24683\n",
      "all done trying2increase usage of unit 2911 final usage = 0.98784 5647 sec elapsed 35 8 complete, failed patches, unstarted= 19\n",
      "current avg and SD of usage are 1.02214 0.24633\n",
      "all done trying2increase usage of unit 2937 final usage = 0.86859 5654 sec elapsed 20 41 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02214 0.24618\n",
      "all done trying2increase usage of unit 382 final usage = 0.98968 5658 sec elapsed 27 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.02215 0.24572\n",
      "all done trying2increase usage of unit 3111 final usage = 0.62852 5691 sec elapsed 1 99 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.02216 0.2457\n",
      "all done trying2increase usage of unit 1844 final usage = 0.61412 5694 sec elapsed 0 7 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.02216 0.2457\n",
      "all done trying2increase usage of unit 1520 final usage = 0.61466 5702 sec elapsed 0 24 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.02216 0.2457\n",
      "all done trying2increase usage of unit 277 final usage = 0.99193 5719 sec elapsed 39 0 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.02219 0.24547\n",
      "all done trying2increase usage of unit 1598 final usage = 0.66925 5732 sec elapsed 5 67 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.02221 0.24544\n",
      "begin usage increase for unit 3319 with usage 0.6156 . Sec, total UU units tried = 5732 300\n",
      "all done trying2increase usage of unit 3319 final usage = 0.6156 5761 sec elapsed 0 33 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.02221 0.24544\n"
     ]
    }
   ],
   "source": [
    "print(\"For now, we will disallow (freeze) HDs from patching.\")\n",
    "print(\"Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\")\n",
    "\n",
    "maxSD = 0.07  #0.055   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = 0.98 #float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "maxExchangePop = 0.25*aDP #this is widened vs. vanillaHD to overcome blockiness\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "debug1 = 10 #int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop),\"= aDP*\",r3(maxExchangePop/aDP) ) \n",
    "maxGap = aDP - minDistrictPop #0.9 * np.median(unitPop)   #aDP - minDistrictPop  \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list(  set(popHDlist).difference( set(hasSplitUnit) )  )\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries:\n",
    "    #simplified vs. vanillaHD - always work on the lowest-used unit.\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedSmallUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = 999.  #preventing re-selection\n",
    "    UUU = eligUnitUse.index(np.min(eligUnitUse))\n",
    "    attemptedSmallUs.append(UUU)\n",
    "    UUUc = [UUU] + unitNbrs[UUU]\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists, UUUcSet = list(), list(), set(UUUc)\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUcSet) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 40 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(UUUcSet)\n",
    "        tryPop = np.sum([unitPop[u] for u in trySet])\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig and tryPop < aDP + maxExchangePop): \n",
    "            #we can't add whole cluster; neighbors may be enclavy or too much pop.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates + [homeU[t]]:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            currPop = np.sum([ unitPop[u] for u in trySet] )\n",
    "            if abs(currPop - aDP) <= maxGap: \n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying2increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"complete, failed patches, unstarted=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "4dda3b0c-37db-41e4-88f9-96f389c6ef32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#11-Dec stop after ~8 hrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6f7f2f60-aa6c-4d46-b2e3-7d2f68841e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 1.02221 and SD 0.24544\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    if t in hasSplitUnit: #hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6de20f70-be39-4abf-ae8b-61483239e04a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unitUse by unitPop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "underPatchedUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "plt.scatter(unitPop,patchedUse,label=\"patched\")\n",
    "#plt.scatter(unitPop,unpatchedUnitUse,label=\"unpatched\")\n",
    "print(\"unitUse by unitPop\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2f7099d0-04c9-4059-9489-c29e075dba0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "save these underpatched results to temp file\n"
     ]
    }
   ],
   "source": [
    "print(\"save these underpatched results to temp file\") #for 1850-99, this is a partial underp to 0.245SD\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"COI1850underP0245.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2a76a448-e301-4762-b6a5-0974c6b4a583",
   "metadata": {},
   "source": [
    "#on cold restart, read in the unit topology\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/OH15COI1850-unitTopologiesBG_10Dec.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "cantSplit = topDF[\"cantSplit\"]\n",
    "unitCountyNo = topDF[\"unitCountyNo\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "borderUnits = list(borderSet) #nomenclature\n",
    "\n",
    "nbrListString = topDF[\"unitNbrs\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "uTLstring = topDF[\"unitTractList\"]\n",
    "unitTractList = [ast.literal_eval(uTLstring[u]) for u in range(nUnits)]\n",
    "allUnits = [u for u in range(nUnits)] #topDF[\"allUnits\"]\n",
    "nDistricts = int(input(\"enter the number of districts in the state map\"))\n",
    "statePop = np.sum(unitPop)\n",
    "aDP = statePop / float(nDistricts)\n",
    "minDPR = float(input(\"enter the ratio of minDistrictPop to aDP; e.g. 0.95\"))\n",
    "minDistrictPop = minDPR * aDP\n",
    "maxDistrictPop = 2*aDP - minDistrictPop\n",
    "print(\"there are\",r3(aDP),\"people in each of the\",nDistricts,\"districts in the state map\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0b4f06d7-cada-40d8-9c58-b751cf905815",
   "metadata": {},
   "source": [
    "############ OPTIONAL RESTART *******\n",
    "print(\"optional restart.  Read in unpatched HDunit lists\")\n",
    "inDF = pd.read_csv(\"./2024state_HD_output/OH8941_15COI1850underP.csv\") #OH8941_15COI1850underP.csv\")\n",
    "## note: for this ipynb, we restarted after all but 3 HDs have good pops, but before patching started\n",
    "HDweight, homeU, homeC = inDF[\"HDweight\"],inDF[\"homeU\"],inDF[\"homeC\"]\n",
    "nHDs = len(HDweight)\n",
    "if \"splitUnitNo\" in inDF.columns.values:\n",
    "    splitUnitNo, splitUnitFrac = inDF[\"splitUnitNo\"],inDF[\"splitUnitFrac\"]\n",
    "else:\n",
    "    splitUnitNo, splitUnitFrac = [-999]*nHDs,[0.]*nHDs\n",
    "hasSplitUnit = list()\n",
    "popHDlist = list()\n",
    "for h in range(nHDs):\n",
    "    if HDweight[h] > 0:\n",
    "        popHDlist.append(h)\n",
    "    if splitUnitNo[h] >= 0:\n",
    "        hasSplitUnit.append(h)\n",
    "unitString = inDF[\"HDunitList\"]\n",
    "HDunitList = [ast.literal_eval(unitString[h]) for h in range(nHDs)]\n",
    "print(\"I read in a total of\",nHDs,\"home district unit lists, of which\",len(popHDlist),\"are populated\")\n",
    "HDvPop = inDF[\"HDvPop\"].to_numpy()\n",
    "HDCPx, HDCPy = inDF[\"centroid x\"],inDF[\"centroid y\"]\n",
    "HDCP = [Point(HDCPx[h],HDCPy[h]) for h in range(nHDs)]\n",
    "hdCP = HDCP.copy()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d00c6f1b-6df1-4998-bdb6-c111b763fa62",
   "metadata": {},
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "96d7db0f-8e24-4a4f-8fa9-09f2376d05c3",
   "metadata": {},
   "source": [
    "minDistrictPop, maxDistrictPop = 0.995*aDP, 1.005*aDP"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4bdd05d6-0f9a-4a93-87d3-6f115b14c0d8",
   "metadata": {},
   "source": [
    "#HDunitList = [underPatchedUnitList[t].copy() for t in range(nHDs)] #restart\n",
    "unitUse, HDvPop = [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "print(\"here is your unitUse histogram prior to more patching\")\n",
    "plt.hist(unitUse,weights=unitPop,bins=20)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist],weights=[HDweight[t] for t in popHDlist])\n",
    "plt.axvline(aDP)\n",
    "plt.axvline(minDistrictPop,ls=\"--\")\n",
    "plt.axvline(maxDistrictPop,ls=\"--\")\n",
    "print(\"and the HD pops\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6e54e08e-d07a-418f-b730-7bbc9d336a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08 = default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxSD 0.08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 549 units out of 3438 with usage above 1.02\n",
      "maxExchangePop is currently 0.15 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 17877\n",
      "current avg and SD of unit usage are 1.02221 0.24544 . Now trying to reduce up to 549 units' overusage\n",
      "starting to reduce usage for unit 830 with usage 2.19106 . Total units tried = 1\n",
      "try to drop unit 830 from 0 th HD out of 143 possible.  usage down to 2.1910647854028373\n",
      "try to drop unit 830 from 30 th HD out of 143 possible.  usage down to 2.043103202793244\n",
      "try to drop unit 830 from 60 th HD out of 143 possible.  usage down to 1.8531149931753443\n",
      "try to drop unit 830 from 90 th HD out of 143 possible.  usage down to 1.7123941730149717\n",
      "all done trying to reduce usage of unit 830 final usage = 1.60743 8 sec elapsed 58 0 successful, failed patches, couldn't start= 57\n",
      "current avg and SD of usage are 1.02194 0.23141\n",
      "starting to reduce usage for unit 1387 with usage 2.02965 . Total units tried = 2\n",
      "try to drop unit 1387 from 0 th HD out of 139 possible.  usage down to 2.0296452851008\n",
      "try to drop unit 1387 from 30 th HD out of 139 possible.  usage down to 1.9130211853971795\n",
      "try to drop unit 1387 from 60 th HD out of 139 possible.  usage down to 1.8549188911203824\n",
      "try to drop unit 1387 from 90 th HD out of 139 possible.  usage down to 1.8440367718890485\n",
      "all done trying to reduce usage of unit 1387 final usage = 1.80303 9 sec elapsed 20 10 successful, failed patches, couldn't start= 82\n",
      "current avg and SD of usage are 1.02183 0.2277\n",
      "starting to reduce usage for unit 1281 with usage 1.89352 . Total units tried = 3\n",
      "try to drop unit 1281 from 0 th HD out of 162 possible.  usage down to 1.8935223071448224\n",
      "try to drop unit 1281 from 30 th HD out of 162 possible.  usage down to 1.5974145570196376\n",
      "try to drop unit 1281 from 60 th HD out of 162 possible.  usage down to 1.4505269229540727\n",
      "try to drop unit 1281 from 90 th HD out of 162 possible.  usage down to 1.4064195206416965\n",
      "try to drop unit 1281 from 120 th HD out of 162 possible.  usage down to 1.239881984309523\n",
      "all done trying to reduce usage of unit 1281 final usage = 1.205 42 sec elapsed 80 6 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.02137 0.21414\n",
      "starting to reduce usage for unit 2489 with usage 1.81358 . Total units tried = 4\n",
      "try to drop unit 2489 from 0 th HD out of 124 possible.  usage down to 1.8135802623982629\n",
      "try to drop unit 2489 from 30 th HD out of 124 possible.  usage down to 1.5782512876869432\n",
      "try to drop unit 2489 from 60 th HD out of 124 possible.  usage down to 1.4145832076208638\n",
      "try to drop unit 2489 from 90 th HD out of 124 possible.  usage down to 1.275364237377551\n",
      "all done trying to reduce usage of unit 2489 final usage = 1.25802 83 sec elapsed 58 0 successful, failed patches, couldn't start= 42\n",
      "current avg and SD of usage are 1.02108 0.20202\n",
      "starting to reduce usage for unit 3436 with usage 1.75954 . Total units tried = 5\n",
      "try to drop unit 3436 from 0 th HD out of 102 possible.  usage down to 1.7595388360536914\n",
      "try to drop unit 3436 from 30 th HD out of 102 possible.  usage down to 1.7305506155877888\n",
      "try to drop unit 3436 from 60 th HD out of 102 possible.  usage down to 1.70322365927594\n",
      "all done trying to reduce usage of unit 3436 final usage = 1.65983 87 sec elapsed 11 1 successful, failed patches, couldn't start= 70\n",
      "current avg and SD of usage are 1.02104 0.19909\n",
      "starting to reduce usage for unit 449 with usage 1.72778 . Total units tried = 6\n",
      "try to drop unit 449 from 0 th HD out of 89 possible.  usage down to 1.7277818419975364\n",
      "try to drop unit 449 from 30 th HD out of 89 possible.  usage down to 1.6417568855758866\n",
      "try to drop unit 449 from 60 th HD out of 89 possible.  usage down to 1.6293896968735004\n",
      "all done trying to reduce usage of unit 449 final usage = 1.62939 87 sec elapsed 8 12 successful, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.02099 0.19614\n",
      "starting to reduce usage for unit 3200 with usage 1.62992 . Total units tried = 7\n",
      "try to drop unit 3200 from 0 th HD out of 156 possible.  usage down to 1.6299182809225723\n",
      "try to drop unit 3200 from 30 th HD out of 156 possible.  usage down to 1.4546213517781477\n",
      "try to drop unit 3200 from 60 th HD out of 156 possible.  usage down to 1.2288991824021505\n",
      "try to drop unit 3200 from 90 th HD out of 156 possible.  usage down to 1.0306549933520193\n",
      "all done trying to reduce usage of unit 3200 final usage = 1.01653 90 sec elapsed 57 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.02072 0.19582\n",
      "starting to reduce usage for unit 592 with usage 1.62079 . Total units tried = 8\n",
      "try to drop unit 592 from 0 th HD out of 94 possible.  usage down to 1.6207897182982505\n",
      "try to drop unit 592 from 30 th HD out of 94 possible.  usage down to 1.5166754410880203\n",
      "try to drop unit 592 from 60 th HD out of 94 possible.  usage down to 1.4832236219859871\n",
      "all done trying to reduce usage of unit 592 final usage = 1.48322 92 sec elapsed 8 0 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.02064 0.19486\n",
      "starting to reduce usage for unit 587 with usage 1.56067 . Total units tried = 9\n",
      "try to drop unit 587 from 0 th HD out of 102 possible.  usage down to 1.560673770501431\n",
      "try to drop unit 587 from 30 th HD out of 102 possible.  usage down to 1.475513007048783\n",
      "try to drop unit 587 from 60 th HD out of 102 possible.  usage down to 1.4577257342882266\n",
      "all done trying to reduce usage of unit 587 final usage = 1.44637 96 sec elapsed 12 0 successful, failed patches, couldn't start= 70\n",
      "current avg and SD of usage are 1.02056 0.19275\n",
      "starting to reduce usage for unit 1801 with usage 1.55138 . Total units tried = 10\n",
      "try to drop unit 1801 from 0 th HD out of 145 possible.  usage down to 1.5513774034171524\n",
      "try to drop unit 1801 from 30 th HD out of 145 possible.  usage down to 1.4790369007089117\n",
      "try to drop unit 1801 from 60 th HD out of 145 possible.  usage down to 1.4118144340308338\n",
      "try to drop unit 1801 from 90 th HD out of 145 possible.  usage down to 1.335119405585451\n",
      "all done trying to reduce usage of unit 1801 final usage = 1.30575 101 sec elapsed 26 0 successful, failed patches, couldn't start= 90\n",
      "current avg and SD of usage are 1.02045 0.19203\n",
      "starting to reduce usage for unit 1187 with usage 1.55093 . Total units tried = 11\n",
      "try to drop unit 1187 from 0 th HD out of 142 possible.  usage down to 1.550932721598125\n",
      "try to drop unit 1187 from 30 th HD out of 142 possible.  usage down to 1.366263913362402\n",
      "try to drop unit 1187 from 60 th HD out of 142 possible.  usage down to 1.277587646472662\n",
      "try to drop unit 1187 from 90 th HD out of 142 possible.  usage down to 1.2552444826229634\n",
      "all done trying to reduce usage of unit 1187 final usage = 1.22862 104 sec elapsed 29 0 successful, failed patches, couldn't start= 85\n",
      "current avg and SD of usage are 1.02035 0.19103\n",
      "starting to reduce usage for unit 514 with usage 1.53851 . Total units tried = 12\n",
      "try to drop unit 514 from 0 th HD out of 89 possible.  usage down to 1.5385068013346337\n",
      "try to drop unit 514 from 30 th HD out of 89 possible.  usage down to 1.3370743275443882\n",
      "try to drop unit 514 from 60 th HD out of 89 possible.  usage down to 1.279735543560608\n",
      "all done trying to reduce usage of unit 514 final usage = 1.26155 105 sec elapsed 19 6 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.02023 0.18847\n",
      "starting to reduce usage for unit 902 with usage 1.48544 . Total units tried = 13\n",
      "try to drop unit 902 from 0 th HD out of 113 possible.  usage down to 1.4854386408580758\n",
      "try to drop unit 902 from 30 th HD out of 113 possible.  usage down to 1.2751880426945579\n",
      "try to drop unit 902 from 60 th HD out of 113 possible.  usage down to 1.1176532156416705\n",
      "all done trying to reduce usage of unit 902 final usage = 1.01178 105 sec elapsed 40 0 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.01996 0.18778\n",
      "starting to reduce usage for unit 1224 with usage 1.47947 . Total units tried = 14\n",
      "try to drop unit 1224 from 0 th HD out of 137 possible.  usage down to 1.479473192304999\n",
      "try to drop unit 1224 from 30 th HD out of 137 possible.  usage down to 1.3802671955499204\n",
      "try to drop unit 1224 from 60 th HD out of 137 possible.  usage down to 1.360961292426321\n",
      "try to drop unit 1224 from 90 th HD out of 137 possible.  usage down to 1.360961292426321\n",
      "all done trying to reduce usage of unit 1224 final usage = 1.36096 109 sec elapsed 12 0 successful, failed patches, couldn't start= 98\n",
      "current avg and SD of usage are 1.01987 0.18746\n",
      "starting to reduce usage for unit 2557 with usage 1.46642 . Total units tried = 15\n",
      "try to drop unit 2557 from 0 th HD out of 99 possible.  usage down to 1.4664180053165663\n",
      "try to drop unit 2557 from 30 th HD out of 99 possible.  usage down to 1.4246934263362647\n",
      "try to drop unit 2557 from 60 th HD out of 99 possible.  usage down to 1.3939264785943133\n",
      "all done trying to reduce usage of unit 2557 final usage = 1.38425 111 sec elapsed 7 0 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.01981 0.18629\n",
      "starting to reduce usage for unit 1725 with usage 1.4576 . Total units tried = 16\n",
      "try to drop unit 1725 from 0 th HD out of 90 possible.  usage down to 1.4575998809432265\n",
      "try to drop unit 1725 from 30 th HD out of 90 possible.  usage down to 1.2074034310754878\n",
      "try to drop unit 1725 from 60 th HD out of 90 possible.  usage down to 1.1481684566936385\n",
      "all done trying to reduce usage of unit 1725 final usage = 1.12225 113 sec elapsed 25 0 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.01971 0.18399\n",
      "starting to reduce usage for unit 516 with usage 1.4366 . Total units tried = 17\n",
      "try to drop unit 516 from 0 th HD out of 79 possible.  usage down to 1.4365991527735036\n",
      "try to drop unit 516 from 30 th HD out of 79 possible.  usage down to 1.2564946258499274\n",
      "try to drop unit 516 from 60 th HD out of 79 possible.  usage down to 1.1371520938942206\n",
      "all done trying to reduce usage of unit 516 final usage = 1.11723 115 sec elapsed 23 24 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.01952 0.18135\n",
      "starting to reduce usage for unit 439 with usage 1.43438 . Total units tried = 18\n",
      "try to drop unit 439 from 0 th HD out of 130 possible.  usage down to 1.4343757436785398\n",
      "try to drop unit 439 from 30 th HD out of 130 possible.  usage down to 1.329120396140267\n",
      "try to drop unit 439 from 60 th HD out of 130 possible.  usage down to 1.329120396140267\n",
      "try to drop unit 439 from 90 th HD out of 130 possible.  usage down to 1.3196562245959709\n",
      "all done trying to reduce usage of unit 439 final usage = 1.31296 116 sec elapsed 13 1 successful, failed patches, couldn't start= 90\n",
      "current avg and SD of usage are 1.01947 0.18063\n",
      "starting to reduce usage for unit 2950 with usage 1.42344 . Total units tried = 19\n",
      "try to drop unit 2950 from 0 th HD out of 127 possible.  usage down to 1.4234432831091195\n",
      "try to drop unit 2950 from 30 th HD out of 127 possible.  usage down to 1.1698823538182552\n",
      "all done trying to reduce usage of unit 2950 final usage = 1.01482 118 sec elapsed 40 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.01921 0.17945\n",
      "starting to reduce usage for unit 152 with usage 1.41708 . Total units tried = 20\n",
      "try to drop unit 152 from 0 th HD out of 71 possible.  usage down to 1.4170751038519556\n",
      "try to drop unit 152 from 30 th HD out of 71 possible.  usage down to 1.392349116670192\n",
      "all done trying to reduce usage of unit 152 final usage = 1.2985 119 sec elapsed 10 5 successful, failed patches, couldn't start= 42\n",
      "current avg and SD of usage are 1.01915 0.17806\n",
      "starting to reduce usage for unit 3437 with usage 1.40825 . Total units tried = 21\n",
      "try to drop unit 3437 from 0 th HD out of 94 possible.  usage down to 1.4082485892555796\n",
      "try to drop unit 3437 from 30 th HD out of 94 possible.  usage down to 1.3417057306405265\n",
      "try to drop unit 3437 from 60 th HD out of 94 possible.  usage down to 1.3229535822349472\n",
      "all done trying to reduce usage of unit 3437 final usage = 1.30278 121 sec elapsed 11 0 successful, failed patches, couldn't start= 65\n",
      "current avg and SD of usage are 1.0191 0.1771\n",
      "starting to reduce usage for unit 384 with usage 1.39016 . Total units tried = 22\n",
      "try to drop unit 384 from 0 th HD out of 111 possible.  usage down to 1.3901592684670527\n",
      "try to drop unit 384 from 30 th HD out of 111 possible.  usage down to 1.3122812185786268\n",
      "try to drop unit 384 from 60 th HD out of 111 possible.  usage down to 1.2793579835256057\n",
      "all done trying to reduce usage of unit 384 final usage = 1.23948 123 sec elapsed 14 1 successful, failed patches, couldn't start= 74\n",
      "current avg and SD of usage are 1.01899 0.17663\n",
      "starting to reduce usage for unit 1411 with usage 1.38568 . Total units tried = 23\n",
      "try to drop unit 1411 from 0 th HD out of 148 possible.  usage down to 1.38567888938509\n",
      "try to drop unit 1411 from 30 th HD out of 148 possible.  usage down to 1.1648314395722237\n",
      "all done trying to reduce usage of unit 1411 final usage = 1.0181 123 sec elapsed 42 4 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.01879 0.17579\n",
      "starting to reduce usage for unit 3130 with usage 1.36834 . Total units tried = 24\n",
      "try to drop unit 3130 from 0 th HD out of 116 possible.  usage down to 1.3683362984435357\n",
      "try to drop unit 3130 from 30 th HD out of 116 possible.  usage down to 1.2705985907135016\n",
      "try to drop unit 3130 from 60 th HD out of 116 possible.  usage down to 1.2587935469521379\n",
      "try to drop unit 3130 from 90 th HD out of 116 possible.  usage down to 1.2587935469521379\n",
      "all done trying to reduce usage of unit 3130 final usage = 1.25879 124 sec elapsed 13 7 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.01872 0.17505\n",
      "starting to reduce usage for unit 769 with usage 1.36274 . Total units tried = 25\n",
      "try to drop unit 769 from 0 th HD out of 110 possible.  usage down to 1.3627400197022526\n",
      "try to drop unit 769 from 30 th HD out of 110 possible.  usage down to 1.2591459363180455\n",
      "try to drop unit 769 from 60 th HD out of 110 possible.  usage down to 1.1909166428799363\n",
      "all done trying to reduce usage of unit 769 final usage = 1.12554 126 sec elapsed 18 1 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.01862 0.17485\n",
      "starting to reduce usage for unit 467 with usage 1.35852 . Total units tried = 26\n",
      "try to drop unit 467 from 0 th HD out of 119 possible.  usage down to 1.35851973753307\n",
      "try to drop unit 467 from 30 th HD out of 119 possible.  usage down to 1.305762015307444\n",
      "try to drop unit 467 from 60 th HD out of 119 possible.  usage down to 1.278258864312595\n",
      "try to drop unit 467 from 90 th HD out of 119 possible.  usage down to 1.2606981275730458\n",
      "all done trying to reduce usage of unit 467 final usage = 1.2607 126 sec elapsed 11 0 successful, failed patches, couldn't start= 85\n",
      "current avg and SD of usage are 1.01854 0.17455\n",
      "starting to reduce usage for unit 1713 with usage 1.35762 . Total units tried = 27\n",
      "try to drop unit 1713 from 0 th HD out of 57 possible.  usage down to 1.3576219836720704\n",
      "try to drop unit 1713 from 30 th HD out of 57 possible.  usage down to 1.2291844499840843\n",
      "all done trying to reduce usage of unit 1713 final usage = 1.22918 127 sec elapsed 9 17 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.01847 0.17284\n",
      "starting to reduce usage for unit 814 with usage 1.32961 . Total units tried = 28\n",
      "try to drop unit 814 from 0 th HD out of 90 possible.  usage down to 1.3296070290742548\n",
      "try to drop unit 814 from 30 th HD out of 90 possible.  usage down to 1.3296070290742548\n",
      "try to drop unit 814 from 60 th HD out of 90 possible.  usage down to 1.3296070290742548\n",
      "all done trying to reduce usage of unit 814 final usage = 1.32961 128 sec elapsed 0 0 successful, failed patches, couldn't start= 72\n",
      "current avg and SD of usage are 1.01847 0.17284\n",
      "starting to reduce usage for unit 641 with usage 1.32945 . Total units tried = 29\n",
      "try to drop unit 641 from 0 th HD out of 112 possible.  usage down to 1.3294476148372236\n",
      "try to drop unit 641 from 30 th HD out of 112 possible.  usage down to 1.135230732827185\n",
      "try to drop unit 641 from 60 th HD out of 112 possible.  usage down to 1.0327609393250186\n",
      "all done trying to reduce usage of unit 641 final usage = 1.01305 130 sec elapsed 30 0 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.01836 0.17226\n",
      "starting to reduce usage for unit 3058 with usage 1.32034 . Total units tried = 30\n",
      "try to drop unit 3058 from 0 th HD out of 108 possible.  usage down to 1.3203442228818905\n",
      "try to drop unit 3058 from 30 th HD out of 108 possible.  usage down to 1.207285967953391\n",
      "try to drop unit 3058 from 60 th HD out of 108 possible.  usage down to 1.176770726901442\n",
      "all done trying to reduce usage of unit 3058 final usage = 1.17677 131 sec elapsed 14 0 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.01827 0.1719\n",
      "starting to reduce usage for unit 1792 with usage 1.31925 . Total units tried = 31\n",
      "try to drop unit 1792 from 0 th HD out of 138 possible.  usage down to 1.3192534938920033\n",
      "try to drop unit 1792 from 30 th HD out of 138 possible.  usage down to 1.102114522645277\n",
      "all done trying to reduce usage of unit 1792 final usage = 1.01102 132 sec elapsed 30 0 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.01818 0.1712\n",
      "starting to reduce usage for unit 1301 with usage 1.30576 . Total units tried = 32\n",
      "try to drop unit 1301 from 0 th HD out of 78 possible.  usage down to 1.3057620153074518\n",
      "try to drop unit 1301 from 30 th HD out of 78 possible.  usage down to 1.1456178289014831\n",
      "try to drop unit 1301 from 60 th HD out of 78 possible.  usage down to 1.0326099153109949\n",
      "all done trying to reduce usage of unit 1301 final usage = 1.02035 132 sec elapsed 22 6 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.01803 0.16953\n",
      "starting to reduce usage for unit 628 with usage 1.27763 . Total units tried = 33\n",
      "try to drop unit 628 from 0 th HD out of 31 possible.  usage down to 1.2776295975875245\n",
      "all done trying to reduce usage of unit 628 final usage = 1.23907 133 sec elapsed 3 1 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.01802 0.16872\n",
      "starting to reduce usage for unit 2148 with usage 1.26995 . Total units tried = 34\n",
      "try to drop unit 2148 from 0 th HD out of 97 possible.  usage down to 1.2699525435424122\n",
      "try to drop unit 2148 from 30 th HD out of 97 possible.  usage down to 1.1122918631445382\n",
      "all done trying to reduce usage of unit 2148 final usage = 1.00918 133 sec elapsed 23 0 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 1.01786 0.16824\n",
      "starting to reduce usage for unit 391 with usage 1.26937 . Total units tried = 35\n",
      "try to drop unit 391 from 0 th HD out of 102 possible.  usage down to 1.2693736181552577\n",
      "try to drop unit 391 from 30 th HD out of 102 possible.  usage down to 1.039993311551169\n",
      "all done trying to reduce usage of unit 391 final usage = 1.01679 135 sec elapsed 21 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.01774 0.16792\n",
      "starting to reduce usage for unit 2058 with usage 1.26185 . Total units tried = 36\n",
      "try to drop unit 2058 from 0 th HD out of 148 possible.  usage down to 1.261847588124242\n",
      "try to drop unit 2058 from 30 th HD out of 148 possible.  usage down to 1.0518235259816973\n",
      "all done trying to reduce usage of unit 2058 final usage = 1.00721 136 sec elapsed 30 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0176 0.16767\n",
      "starting to reduce usage for unit 484 with usage 1.26153 . Total units tried = 37\n",
      "try to drop unit 484 from 0 th HD out of 112 possible.  usage down to 1.261528759650144\n",
      "all done trying to reduce usage of unit 484 final usage = 1.01092 136 sec elapsed 22 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01746 0.16735\n",
      "starting to reduce usage for unit 473 with usage 1.25987 . Total units tried = 38\n",
      "try to drop unit 473 from 0 th HD out of 46 possible.  usage down to 1.2598674954960387\n",
      "try to drop unit 473 from 30 th HD out of 46 possible.  usage down to 1.1849931454417306\n",
      "all done trying to reduce usage of unit 473 final usage = 1.18499 138 sec elapsed 7 6 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.01741 0.16621\n",
      "starting to reduce usage for unit 1323 with usage 1.25788 . Total units tried = 39\n",
      "try to drop unit 1323 from 0 th HD out of 89 possible.  usage down to 1.257879012644904\n",
      "all done trying to reduce usage of unit 1323 final usage = 1.01465 139 sec elapsed 17 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01726 0.16562\n",
      "starting to reduce usage for unit 140 with usage 1.25592 . Total units tried = 40\n",
      "try to drop unit 140 from 0 th HD out of 105 possible.  usage down to 1.2559157004629866\n",
      "try to drop unit 140 from 30 th HD out of 105 possible.  usage down to 1.0911149402918572\n",
      "all done trying to reduce usage of unit 140 final usage = 1.00689 141 sec elapsed 23 0 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.0171 0.16508\n",
      "starting to reduce usage for unit 3032 with usage 1.25323 . Total units tried = 41\n",
      "try to drop unit 3032 from 0 th HD out of 86 possible.  usage down to 1.2532308291028658\n",
      "try to drop unit 3032 from 30 th HD out of 86 possible.  usage down to 1.1513231805417108\n",
      "try to drop unit 3032 from 60 th HD out of 86 possible.  usage down to 1.0728074737052578\n",
      "all done trying to reduce usage of unit 3032 final usage = 1.05468 144 sec elapsed 20 9 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 1.01696 0.16395\n",
      "starting to reduce usage for unit 2220 with usage 1.23377 . Total units tried = 42\n",
      "try to drop unit 2220 from 0 th HD out of 92 possible.  usage down to 1.2337655117422361\n",
      "try to drop unit 2220 from 30 th HD out of 92 possible.  usage down to 1.2197790100008041\n",
      "try to drop unit 2220 from 60 th HD out of 92 possible.  usage down to 1.197016335001102\n",
      "all done trying to reduce usage of unit 2220 final usage = 1.15065 145 sec elapsed 6 13 successful, failed patches, couldn't start= 55\n",
      "current avg and SD of usage are 1.0169 0.1637\n",
      "starting to reduce usage for unit 386 with usage 1.23128 . Total units tried = 43\n",
      "try to drop unit 386 from 0 th HD out of 73 possible.  usage down to 1.2312820057341585\n",
      "try to drop unit 386 from 30 th HD out of 73 possible.  usage down to 1.0478465602794707\n",
      "all done trying to reduce usage of unit 386 final usage = 1.03785 145 sec elapsed 14 0 successful, failed patches, couldn't start= 45\n",
      "current avg and SD of usage are 1.01684 0.16312\n",
      "starting to reduce usage for unit 1710 with usage 1.22011 . Total units tried = 44\n",
      "try to drop unit 1710 from 0 th HD out of 78 possible.  usage down to 1.2201062286977682\n",
      "try to drop unit 1710 from 30 th HD out of 78 possible.  usage down to 1.058007120332746\n",
      "all done trying to reduce usage of unit 1710 final usage = 1.01289 146 sec elapsed 18 2 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.01676 0.1623\n",
      "starting to reduce usage for unit 1869 with usage 1.21902 . Total units tried = 45\n",
      "try to drop unit 1869 from 0 th HD out of 81 possible.  usage down to 1.2190238899306718\n",
      "all done trying to reduce usage of unit 1869 final usage = 1.01462 149 sec elapsed 18 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.01663 0.16182\n",
      "starting to reduce usage for unit 1558 with usage 1.21507 . Total units tried = 46\n",
      "try to drop unit 1558 from 0 th HD out of 96 possible.  usage down to 1.2150720948976788\n",
      "try to drop unit 1558 from 30 th HD out of 96 possible.  usage down to 1.0825401323855637\n",
      "try to drop unit 1558 from 60 th HD out of 96 possible.  usage down to 1.058309168360815\n",
      "all done trying to reduce usage of unit 1558 final usage = 1.05831 151 sec elapsed 16 5 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.01654 0.16145\n",
      "starting to reduce usage for unit 1554 with usage 1.20494 . Total units tried = 47\n",
      "try to drop unit 1554 from 0 th HD out of 139 possible.  usage down to 1.204936705513476\n",
      "all done trying to reduce usage of unit 1554 final usage = 1.01843 152 sec elapsed 20 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01646 0.1614\n",
      "starting to reduce usage for unit 1712 with usage 1.20192 . Total units tried = 48\n",
      "try to drop unit 1712 from 0 th HD out of 87 possible.  usage down to 1.2019246154562626\n",
      "try to drop unit 1712 from 30 th HD out of 87 possible.  usage down to 1.0239847660667363\n",
      "all done trying to reduce usage of unit 1712 final usage = 1.01376 152 sec elapsed 12 18 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01636 0.1612\n",
      "starting to reduce usage for unit 1737 with usage 1.20091 . Total units tried = 49\n",
      "try to drop unit 1737 from 0 th HD out of 106 possible.  usage down to 1.200909398473343\n",
      "try to drop unit 1737 from 30 th HD out of 106 possible.  usage down to 1.1261357310950157\n",
      "all done trying to reduce usage of unit 1737 final usage = 1.01452 152 sec elapsed 21 4 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.01629 0.16054\n",
      "starting to reduce usage for unit 569 with usage 1.19628 . Total units tried = 50\n",
      "try to drop unit 569 from 0 th HD out of 79 possible.  usage down to 1.1962779953770497\n",
      "try to drop unit 569 from 30 th HD out of 79 possible.  usage down to 1.0314352840909506\n",
      "all done trying to reduce usage of unit 569 final usage = 1.01256 153 sec elapsed 15 11 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.0162 0.16002\n",
      "starting to reduce usage for unit 401 with usage 1.19311 . Total units tried = 51\n",
      "try to drop unit 401 from 0 th HD out of 100 possible.  usage down to 1.193106491082933\n",
      "try to drop unit 401 from 30 th HD out of 100 possible.  usage down to 1.113399372580467\n",
      "try to drop unit 401 from 60 th HD out of 100 possible.  usage down to 1.0997820406510448\n",
      "all done trying to reduce usage of unit 401 final usage = 1.03863 153 sec elapsed 14 3 successful, failed patches, couldn't start= 63\n",
      "current avg and SD of usage are 1.01609 0.15985\n",
      "starting to reduce usage for unit 2062 with usage 1.18917 . Total units tried = 52\n",
      "try to drop unit 2062 from 0 th HD out of 94 possible.  usage down to 1.189171476495814\n",
      "all done trying to reduce usage of unit 2062 final usage = 1.00949 154 sec elapsed 13 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.016 0.15961\n",
      "starting to reduce usage for unit 2584 with usage 1.18549 . Total units tried = 53\n",
      "try to drop unit 2584 from 0 th HD out of 86 possible.  usage down to 1.1854881685986594\n",
      "try to drop unit 2584 from 30 th HD out of 86 possible.  usage down to 1.085283735306557\n",
      "all done trying to reduce usage of unit 2584 final usage = 1.01388 154 sec elapsed 15 0 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.01594 0.1593\n",
      "starting to reduce usage for unit 912 with usage 1.18535 . Total units tried = 54\n",
      "try to drop unit 912 from 0 th HD out of 76 possible.  usage down to 1.185345534807785\n",
      "all done trying to reduce usage of unit 912 final usage = 1.01831 154 sec elapsed 13 0 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.01589 0.15888\n",
      "starting to reduce usage for unit 420 with usage 1.18227 . Total units tried = 55\n",
      "try to drop unit 420 from 0 th HD out of 80 possible.  usage down to 1.18227471318956\n",
      "all done trying to reduce usage of unit 420 final usage = 1.01961 155 sec elapsed 13 1 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.01579 0.15857\n",
      "starting to reduce usage for unit 402 with usage 1.1821 . Total units tried = 56\n",
      "try to drop unit 402 from 0 th HD out of 85 possible.  usage down to 1.1820985185066692\n",
      "all done trying to reduce usage of unit 402 final usage = 1.00974 156 sec elapsed 13 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01574 0.15827\n",
      "starting to reduce usage for unit 987 with usage 1.18061 . Total units tried = 57\n",
      "try to drop unit 987 from 0 th HD out of 81 possible.  usage down to 1.1806134490356173\n",
      "all done trying to reduce usage of unit 987 final usage = 0.99602 156 sec elapsed 12 3 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.01562 0.15802\n",
      "starting to reduce usage for unit 3229 with usage 1.17672 . Total units tried = 58\n",
      "try to drop unit 3229 from 0 th HD out of 111 possible.  usage down to 1.1767203855634714\n",
      "all done trying to reduce usage of unit 3229 final usage = 1.01634 157 sec elapsed 16 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01553 0.15789\n",
      "starting to reduce usage for unit 454 with usage 1.17597 . Total units tried = 59\n",
      "try to drop unit 454 from 0 th HD out of 101 possible.  usage down to 1.1759652654934367\n",
      "all done trying to reduce usage of unit 454 final usage = 1.01815 157 sec elapsed 14 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.01545 0.15765\n",
      "starting to reduce usage for unit 3047 with usage 1.17317 . Total units tried = 60\n",
      "try to drop unit 3047 from 0 th HD out of 111 possible.  usage down to 1.173171321234445\n",
      "all done trying to reduce usage of unit 3047 final usage = 1.01861 158 sec elapsed 15 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01536 0.15756\n",
      "starting to reduce usage for unit 388 with usage 1.17188 . Total units tried = 61\n",
      "try to drop unit 388 from 0 th HD out of 89 possible.  usage down to 1.1718792268923064\n",
      "all done trying to reduce usage of unit 388 final usage = 1.01134 158 sec elapsed 15 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.0153 0.15754\n",
      "starting to reduce usage for unit 563 with usage 1.16759 . Total units tried = 62\n",
      "try to drop unit 563 from 0 th HD out of 96 possible.  usage down to 1.1675918229391653\n",
      "try to drop unit 563 from 30 th HD out of 96 possible.  usage down to 1.0467306606204148\n",
      "all done trying to reduce usage of unit 563 final usage = 1.01773 158 sec elapsed 15 1 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.01524 0.15733\n",
      "starting to reduce usage for unit 693 with usage 1.16558 . Total units tried = 63\n",
      "try to drop unit 693 from 0 th HD out of 75 possible.  usage down to 1.1655781694190575\n",
      "try to drop unit 693 from 30 th HD out of 75 possible.  usage down to 1.0742841529532445\n",
      "all done trying to reduce usage of unit 693 final usage = 1.06555 159 sec elapsed 8 0 successful, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.01518 0.15719\n",
      "starting to reduce usage for unit 2952 with usage 1.15467 . Total units tried = 64\n",
      "try to drop unit 2952 from 0 th HD out of 93 possible.  usage down to 1.1546708795188403\n",
      "all done trying to reduce usage of unit 2952 final usage = 1.01661 159 sec elapsed 14 10 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0151 0.15706\n",
      "starting to reduce usage for unit 1325 with usage 1.15032 . Total units tried = 65\n",
      "try to drop unit 1325 from 0 th HD out of 97 possible.  usage down to 1.1503247440045117\n",
      "all done trying to reduce usage of unit 1325 final usage = 1.00685 161 sec elapsed 12 8 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01508 0.15692\n",
      "starting to reduce usage for unit 2880 with usage 1.14762 . Total units tried = 66\n",
      "try to drop unit 2880 from 0 th HD out of 95 possible.  usage down to 1.147623092198469\n",
      "all done trying to reduce usage of unit 2880 final usage = 1.01981 161 sec elapsed 10 0 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.015 0.15679\n",
      "starting to reduce usage for unit 2465 with usage 1.14409 . Total units tried = 67\n",
      "try to drop unit 2465 from 0 th HD out of 75 possible.  usage down to 1.144090808315392\n",
      "all done trying to reduce usage of unit 2465 final usage = 1.01045 162 sec elapsed 11 4 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01499 0.15667\n",
      "starting to reduce usage for unit 1276 with usage 1.14116 . Total units tried = 68\n",
      "try to drop unit 1276 from 0 th HD out of 116 possible.  usage down to 1.141162620488406\n",
      "all done trying to reduce usage of unit 1276 final usage = 1.01666 163 sec elapsed 13 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01499 0.15664\n",
      "starting to reduce usage for unit 728 with usage 1.13821 . Total units tried = 69\n",
      "try to drop unit 728 from 0 th HD out of 45 possible.  usage down to 1.138209261992342\n",
      "all done trying to reduce usage of unit 728 final usage = 1.01163 191 sec elapsed 15 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01492 0.15548\n",
      "starting to reduce usage for unit 2582 with usage 1.13005 . Total units tried = 70\n",
      "try to drop unit 2582 from 0 th HD out of 95 possible.  usage down to 1.1300539652360269\n",
      "all done trying to reduce usage of unit 2582 final usage = 1.00935 192 sec elapsed 10 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01489 0.15539\n",
      "starting to reduce usage for unit 650 with usage 1.12986 . Total units tried = 71\n",
      "try to drop unit 650 from 0 th HD out of 81 possible.  usage down to 1.1298609901070085\n",
      "all done trying to reduce usage of unit 650 final usage = 1.01575 192 sec elapsed 9 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.01482 0.15524\n",
      "starting to reduce usage for unit 1299 with usage 1.1284 . Total units tried = 72\n",
      "try to drop unit 1299 from 0 th HD out of 85 possible.  usage down to 1.1284010913049463\n",
      "all done trying to reduce usage of unit 1299 final usage = 1.00856 192 sec elapsed 10 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01477 0.15517\n",
      "starting to reduce usage for unit 406 with usage 1.12796 . Total units tried = 73\n",
      "try to drop unit 406 from 0 th HD out of 84 possible.  usage down to 1.1279564094859678\n",
      "all done trying to reduce usage of unit 406 final usage = 1.01033 192 sec elapsed 9 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01472 0.15502\n",
      "starting to reduce usage for unit 962 with usage 1.12583 . Total units tried = 74\n",
      "try to drop unit 962 from 0 th HD out of 78 possible.  usage down to 1.125833683066831\n",
      "try to drop unit 962 from 30 th HD out of 78 possible.  usage down to 1.039716434192161\n",
      "all done trying to reduce usage of unit 962 final usage = 1.0155 193 sec elapsed 9 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.01465 0.15488\n",
      "starting to reduce usage for unit 2956 with usage 1.1254 . Total units tried = 75\n",
      "try to drop unit 2956 from 0 th HD out of 93 possible.  usage down to 1.1253973914709188\n",
      "all done trying to reduce usage of unit 2956 final usage = 1.01644 193 sec elapsed 10 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.01459 0.15476\n",
      "starting to reduce usage for unit 535 with usage 1.12348 . Total units tried = 76\n",
      "try to drop unit 535 from 0 th HD out of 85 possible.  usage down to 1.1234760304038056\n",
      "all done trying to reduce usage of unit 535 final usage = 1.00979 193 sec elapsed 9 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.01453 0.15464\n",
      "starting to reduce usage for unit 721 with usage 1.12167 . Total units tried = 77\n",
      "try to drop unit 721 from 0 th HD out of 85 possible.  usage down to 1.1216721324587227\n",
      "all done trying to reduce usage of unit 721 final usage = 1.01315 194 sec elapsed 10 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.01448 0.15453\n",
      "starting to reduce usage for unit 625 with usage 1.11894 . Total units tried = 78\n",
      "try to drop unit 625 from 0 th HD out of 82 possible.  usage down to 1.1189369197607248\n",
      "all done trying to reduce usage of unit 625 final usage = 1.00929 194 sec elapsed 7 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.0144 0.15444\n",
      "starting to reduce usage for unit 2894 with usage 1.11496 . Total units tried = 79\n",
      "try to drop unit 2894 from 0 th HD out of 96 possible.  usage down to 1.114959954058583\n",
      "all done trying to reduce usage of unit 2894 final usage = 1.0135 194 sec elapsed 7 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01433 0.15438\n",
      "starting to reduce usage for unit 3030 with usage 1.11097 . Total units tried = 80\n",
      "try to drop unit 3030 from 0 th HD out of 87 possible.  usage down to 1.1109662079104115\n",
      "all done trying to reduce usage of unit 3030 final usage = 1.01119 195 sec elapsed 8 3 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01427 0.15428\n",
      "starting to reduce usage for unit 941 with usage 1.1105 . Total units tried = 81\n",
      "try to drop unit 941 from 0 th HD out of 114 possible.  usage down to 1.110504745645555\n",
      "all done trying to reduce usage of unit 941 final usage = 1.01231 195 sec elapsed 11 2 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.01422 0.15421\n",
      "starting to reduce usage for unit 876 with usage 1.1098 . Total units tried = 82\n",
      "try to drop unit 876 from 0 th HD out of 88 possible.  usage down to 1.1097999669133285\n",
      "all done trying to reduce usage of unit 876 final usage = 1.00833 196 sec elapsed 9 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.01413 0.15417\n",
      "starting to reduce usage for unit 58 with usage 1.10651 . Total units tried = 83\n",
      "try to drop unit 58 from 0 th HD out of 84 possible.  usage down to 1.1065109994973041\n",
      "all done trying to reduce usage of unit 58 final usage = 1.01315 196 sec elapsed 6 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01406 0.15408\n",
      "starting to reduce usage for unit 2189 with usage 1.10622 . Total units tried = 84\n",
      "try to drop unit 2189 from 0 th HD out of 65 possible.  usage down to 1.1062173416923204\n",
      "all done trying to reduce usage of unit 2189 final usage = 1.00844 196 sec elapsed 8 3 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01402 0.15383\n",
      "starting to reduce usage for unit 1179 with usage 1.10603 . Total units tried = 85\n",
      "try to drop unit 1179 from 0 th HD out of 87 possible.  usage down to 1.1060327567861623\n",
      "all done trying to reduce usage of unit 1179 final usage = 1.01139 197 sec elapsed 9 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01397 0.15375\n",
      "starting to reduce usage for unit 1102 with usage 1.10328 . Total units tried = 86\n",
      "try to drop unit 1102 from 0 th HD out of 92 possible.  usage down to 1.1032807636421975\n",
      "all done trying to reduce usage of unit 1102 final usage = 1.01 198 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01392 0.15371\n",
      "starting to reduce usage for unit 1418 with usage 1.10209 . Total units tried = 87\n",
      "try to drop unit 1418 from 0 th HD out of 96 possible.  usage down to 1.1020893519762303\n",
      "all done trying to reduce usage of unit 1418 final usage = 1.01942 200 sec elapsed 9 6 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.01386 0.15364\n",
      "starting to reduce usage for unit 2158 with usage 1.10044 . Total units tried = 88\n",
      "all done trying to reduce usage of unit 2158 final usage = 1.10044 200 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01386 0.15364\n",
      "starting to reduce usage for unit 2948 with usage 1.09799 . Total units tried = 89\n",
      "try to drop unit 2948 from 0 th HD out of 85 possible.  usage down to 1.0979949231519555\n",
      "all done trying to reduce usage of unit 2948 final usage = 1.01372 200 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0138 0.15357\n",
      "starting to reduce usage for unit 517 with usage 1.09692 . Total units tried = 90\n",
      "try to drop unit 517 from 0 th HD out of 82 possible.  usage down to 1.0969209746079698\n",
      "all done trying to reduce usage of unit 517 final usage = 1.01549 200 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01374 0.15352\n",
      "starting to reduce usage for unit 3099 with usage 1.09664 . Total units tried = 91\n",
      "try to drop unit 3099 from 0 th HD out of 82 possible.  usage down to 1.0966357070259227\n",
      "all done trying to reduce usage of unit 3099 final usage = 1.00798 201 sec elapsed 7 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.01372 0.15346\n",
      "starting to reduce usage for unit 559 with usage 1.09413 . Total units tried = 92\n",
      "try to drop unit 559 from 0 th HD out of 73 possible.  usage down to 1.0941270303489123\n",
      "all done trying to reduce usage of unit 559 final usage = 1.01367 202 sec elapsed 7 0 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.01368 0.15336\n",
      "starting to reduce usage for unit 284 with usage 1.09148 . Total units tried = 93\n",
      "try to drop unit 284 from 0 th HD out of 64 possible.  usage down to 1.0914841101038624\n",
      "all done trying to reduce usage of unit 284 final usage = 1.0099 202 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01363 0.15325\n",
      "starting to reduce usage for unit 1248 with usage 1.08966 . Total units tried = 94\n",
      "try to drop unit 1248 from 0 th HD out of 83 possible.  usage down to 1.089663431712705\n",
      "all done trying to reduce usage of unit 1248 final usage = 1.01611 202 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01358 0.15323\n",
      "starting to reduce usage for unit 2441 with usage 1.08238 . Total units tried = 95\n",
      "try to drop unit 2441 from 0 th HD out of 93 possible.  usage down to 1.0823807181485607\n",
      "all done trying to reduce usage of unit 2441 final usage = 1.01769 202 sec elapsed 5 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01356 0.15319\n",
      "starting to reduce usage for unit 2953 with usage 1.08086 . Total units tried = 96\n",
      "try to drop unit 2953 from 0 th HD out of 69 possible.  usage down to 1.0808620877854893\n",
      "all done trying to reduce usage of unit 2953 final usage = 1.01256 202 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01353 0.1531\n",
      "starting to reduce usage for unit 2075 with usage 1.08054 . Total units tried = 97\n",
      "try to drop unit 2075 from 0 th HD out of 90 possible.  usage down to 1.0805432593114817\n",
      "all done trying to reduce usage of unit 2075 final usage = 1.01258 203 sec elapsed 7 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.0135 0.15306\n",
      "starting to reduce usage for unit 282 with usage 1.08048 . Total units tried = 98\n",
      "try to drop unit 282 from 0 th HD out of 68 possible.  usage down to 1.080484527750468\n",
      "all done trying to reduce usage of unit 282 final usage = 1.01622 203 sec elapsed 5 2 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.01348 0.15292\n",
      "starting to reduce usage for unit 2181 with usage 1.08017 . Total units tried = 99\n",
      "try to drop unit 2181 from 0 th HD out of 88 possible.  usage down to 1.0801656992765085\n",
      "all done trying to reduce usage of unit 2181 final usage = 1.0172 203 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01346 0.15291\n",
      "starting to reduce usage for unit 529 with usage 1.0796 . Total units tried = 100\n",
      "try to drop unit 529 from 0 th HD out of 93 possible.  usage down to 1.0796035543354274\n",
      "all done trying to reduce usage of unit 529 final usage = 1.00885 203 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01343 0.15291\n",
      "starting to reduce usage for unit 824 with usage 1.07891 . Total units tried = 101\n",
      "try to drop unit 824 from 0 th HD out of 96 possible.  usage down to 1.0789071658264926\n",
      "all done trying to reduce usage of unit 824 final usage = 1.01997 203 sec elapsed 7 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01339 0.15288\n",
      "starting to reduce usage for unit 2639 with usage 1.07752 . Total units tried = 102\n",
      "try to drop unit 2639 from 0 th HD out of 91 possible.  usage down to 1.0775227790313635\n",
      "all done trying to reduce usage of unit 2639 final usage = 1.01722 204 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01337 0.15284\n",
      "starting to reduce usage for unit 1412 with usage 1.07655 . Total units tried = 103\n",
      "try to drop unit 1412 from 0 th HD out of 93 possible.  usage down to 1.0765495131633724\n",
      "all done trying to reduce usage of unit 1412 final usage = 1.0177 204 sec elapsed 4 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01333 0.15282\n",
      "starting to reduce usage for unit 1123 with usage 1.07524 . Total units tried = 104\n",
      "try to drop unit 1123 from 0 th HD out of 87 possible.  usage down to 1.075240638375305\n",
      "all done trying to reduce usage of unit 1123 final usage = 1.00922 204 sec elapsed 5 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01331 0.15277\n",
      "starting to reduce usage for unit 797 with usage 1.07462 . Total units tried = 105\n",
      "try to drop unit 797 from 0 th HD out of 70 possible.  usage down to 1.0746197618731708\n",
      "all done trying to reduce usage of unit 797 final usage = 1.0166 205 sec elapsed 6 2 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01329 0.15264\n",
      "starting to reduce usage for unit 2010 with usage 1.07418 . Total units tried = 106\n",
      "try to drop unit 2010 from 0 th HD out of 93 possible.  usage down to 1.0741834702772968\n",
      "all done trying to reduce usage of unit 2010 final usage = 1.01291 205 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01329 0.1526\n",
      "starting to reduce usage for unit 1205 with usage 1.07382 . Total units tried = 107\n",
      "try to drop unit 1205 from 0 th HD out of 87 possible.  usage down to 1.0738226906882815\n",
      "all done trying to reduce usage of unit 1205 final usage = 1.01361 206 sec elapsed 5 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01325 0.15256\n",
      "starting to reduce usage for unit 1419 with usage 1.07289 . Total units tried = 108\n",
      "try to drop unit 1419 from 0 th HD out of 67 possible.  usage down to 1.0728913759352314\n",
      "all done trying to reduce usage of unit 1419 final usage = 1.01658 206 sec elapsed 5 0 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.01322 0.15247\n",
      "starting to reduce usage for unit 2110 with usage 1.07258 . Total units tried = 109\n",
      "try to drop unit 2110 from 0 th HD out of 90 possible.  usage down to 1.0725809376842772\n",
      "all done trying to reduce usage of unit 2110 final usage = 1.00876 206 sec elapsed 5 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0132 0.15244\n",
      "starting to reduce usage for unit 682 with usage 1.07173 . Total units tried = 110\n",
      "try to drop unit 682 from 0 th HD out of 84 possible.  usage down to 1.0717335251612001\n",
      "all done trying to reduce usage of unit 682 final usage = 1.01213 206 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01315 0.15239\n",
      "starting to reduce usage for unit 952 with usage 1.0711 . Total units tried = 111\n",
      "try to drop unit 952 from 0 th HD out of 84 possible.  usage down to 1.0711042584362196\n",
      "all done trying to reduce usage of unit 952 final usage = 0.98856 206 sec elapsed 6 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01311 0.15228\n",
      "starting to reduce usage for unit 1698 with usage 1.06851 . Total units tried = 112\n",
      "try to drop unit 1698 from 0 th HD out of 100 possible.  usage down to 1.0685116795291814\n",
      "try to drop unit 1698 from 30 th HD out of 100 possible.  usage down to 1.059542531141901\n",
      "all done trying to reduce usage of unit 1698 final usage = 1.01989 207 sec elapsed 5 0 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.01308 0.15225\n",
      "starting to reduce usage for unit 2107 with usage 1.0668 . Total units tried = 113\n",
      "try to drop unit 2107 from 0 th HD out of 113 possible.  usage down to 1.0668000740371055\n",
      "try to drop unit 2107 from 30 th HD out of 113 possible.  usage down to 1.0616904282299457\n",
      "try to drop unit 2107 from 60 th HD out of 113 possible.  usage down to 1.0471753424394916\n",
      "try to drop unit 2107 from 90 th HD out of 113 possible.  usage down to 1.0287923638459193\n",
      "all done trying to reduce usage of unit 2107 final usage = 1.02879 207 sec elapsed 5 0 successful, failed patches, couldn't start= 86\n",
      "current avg and SD of usage are 1.01308 0.1521\n",
      "starting to reduce usage for unit 1827 with usage 1.06614 . Total units tried = 114\n",
      "try to drop unit 1827 from 0 th HD out of 73 possible.  usage down to 1.0661372464200405\n",
      "all done trying to reduce usage of unit 1827 final usage = 1.00497 207 sec elapsed 5 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01305 0.15206\n",
      "starting to reduce usage for unit 945 with usage 1.06579 . Total units tried = 115\n",
      "try to drop unit 945 from 0 th HD out of 120 possible.  usage down to 1.0657932472771254\n",
      "all done trying to reduce usage of unit 945 final usage = 1.01835 207 sec elapsed 6 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01302 0.15205\n",
      "starting to reduce usage for unit 791 with usage 1.06574 . Total units tried = 116\n",
      "try to drop unit 791 from 0 th HD out of 75 possible.  usage down to 1.0657429059390113\n",
      "all done trying to reduce usage of unit 791 final usage = 1.01149 208 sec elapsed 5 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01301 0.15201\n",
      "starting to reduce usage for unit 579 with usage 1.06561 . Total units tried = 117\n",
      "try to drop unit 579 from 0 th HD out of 75 possible.  usage down to 1.0656086623709926\n",
      "all done trying to reduce usage of unit 579 final usage = 1.01432 208 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01298 0.15197\n",
      "starting to reduce usage for unit 490 with usage 1.06497 . Total units tried = 118\n",
      "try to drop unit 490 from 0 th HD out of 91 possible.  usage down to 1.0649710054230335\n",
      "all done trying to reduce usage of unit 490 final usage = 1.00604 208 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01294 0.15189\n",
      "starting to reduce usage for unit 2560 with usage 1.06396 . Total units tried = 119\n",
      "try to drop unit 2560 from 0 th HD out of 98 possible.  usage down to 1.0639557884400284\n",
      "all done trying to reduce usage of unit 2560 final usage = 1.01658 208 sec elapsed 5 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0129 0.15187\n",
      "starting to reduce usage for unit 2641 with usage 1.0639 . Total units tried = 120\n",
      "try to drop unit 2641 from 0 th HD out of 94 possible.  usage down to 1.0638970568789285\n",
      "all done trying to reduce usage of unit 2641 final usage = 1.0133 208 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01286 0.15185\n",
      "starting to reduce usage for unit 2146 with usage 1.06357 . Total units tried = 121\n",
      "try to drop unit 2146 from 0 th HD out of 76 possible.  usage down to 1.0635698381819665\n",
      "all done trying to reduce usage of unit 2146 final usage = 1.01592 209 sec elapsed 3 2 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.01285 0.15179\n",
      "starting to reduce usage for unit 135 with usage 1.06328 . Total units tried = 122\n",
      "try to drop unit 135 from 0 th HD out of 90 possible.  usage down to 1.0632761803769362\n",
      "all done trying to reduce usage of unit 135 final usage = 1.01766 209 sec elapsed 4 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01281 0.15177\n",
      "starting to reduce usage for unit 1744 with usage 1.06323 . Total units tried = 123\n",
      "try to drop unit 1744 from 0 th HD out of 95 possible.  usage down to 1.0632258390389386\n",
      "all done trying to reduce usage of unit 1744 final usage = 1.01607 209 sec elapsed 5 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01279 0.15175\n",
      "starting to reduce usage for unit 2449 with usage 1.0621 . Total units tried = 124\n",
      "try to drop unit 2449 from 0 th HD out of 88 possible.  usage down to 1.0621015491568786\n",
      "all done trying to reduce usage of unit 2449 final usage = 1.0139 209 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01277 0.15174\n",
      "starting to reduce usage for unit 417 with usage 1.06206 . Total units tried = 125\n",
      "try to drop unit 417 from 0 th HD out of 73 possible.  usage down to 1.0620595980417749\n",
      "all done trying to reduce usage of unit 417 final usage = 1.01632 210 sec elapsed 4 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01276 0.15167\n",
      "starting to reduce usage for unit 425 with usage 1.05912 . Total units tried = 126\n",
      "try to drop unit 425 from 0 th HD out of 91 possible.  usage down to 1.059123019991872\n",
      "all done trying to reduce usage of unit 425 final usage = 1.01869 210 sec elapsed 5 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.01275 0.15165\n",
      "starting to reduce usage for unit 1251 with usage 1.05887 . Total units tried = 127\n",
      "try to drop unit 1251 from 0 th HD out of 89 possible.  usage down to 1.0588713133018586\n",
      "all done trying to reduce usage of unit 1251 final usage = 1.0173 210 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01271 0.15165\n",
      "starting to reduce usage for unit 2585 with usage 1.0587 . Total units tried = 128\n",
      "try to drop unit 2585 from 0 th HD out of 105 possible.  usage down to 1.058695118618872\n",
      "all done trying to reduce usage of unit 2585 final usage = 1.01897 211 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01269 0.15163\n",
      "starting to reduce usage for unit 944 with usage 1.05788 . Total units tried = 129\n",
      "try to drop unit 944 from 0 th HD out of 100 possible.  usage down to 1.057881266987814\n",
      "all done trying to reduce usage of unit 944 final usage = 1.01811 211 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01266 0.15162\n",
      "starting to reduce usage for unit 155 with usage 1.05667 . Total units tried = 130\n",
      "try to drop unit 155 from 0 th HD out of 70 possible.  usage down to 1.0566730748757323\n",
      "all done trying to reduce usage of unit 155 final usage = 1.00667 211 sec elapsed 3 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.01265 0.15159\n",
      "starting to reduce usage for unit 3038 with usage 1.05628 . Total units tried = 131\n",
      "try to drop unit 3038 from 0 th HD out of 87 possible.  usage down to 1.0562787343946713\n",
      "all done trying to reduce usage of unit 3038 final usage = 1.01005 211 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01263 0.15153\n",
      "starting to reduce usage for unit 145 with usage 1.05402 . Total units tried = 132\n",
      "try to drop unit 145 from 0 th HD out of 75 possible.  usage down to 1.0540217644076657\n",
      "all done trying to reduce usage of unit 145 final usage = 1.00994 211 sec elapsed 4 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01264 0.15149\n",
      "starting to reduce usage for unit 1296 with usage 1.05317 . Total units tried = 133\n",
      "try to drop unit 1296 from 0 th HD out of 72 possible.  usage down to 1.0531743518846433\n",
      "all done trying to reduce usage of unit 1296 final usage = 1.01186 212 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01264 0.15148\n",
      "starting to reduce usage for unit 2840 with usage 1.05316 . Total units tried = 134\n",
      "try to drop unit 2840 from 0 th HD out of 99 possible.  usage down to 1.0531575714386956\n",
      "all done trying to reduce usage of unit 2840 final usage = 1.01965 212 sec elapsed 3 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.01262 0.15146\n",
      "starting to reduce usage for unit 3094 with usage 1.0528 . Total units tried = 135\n",
      "try to drop unit 3094 from 0 th HD out of 91 possible.  usage down to 1.0527967918496117\n",
      "all done trying to reduce usage of unit 3094 final usage = 1.00767 212 sec elapsed 4 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01259 0.15143\n",
      "starting to reduce usage for unit 576 with usage 1.05244 . Total units tried = 136\n",
      "try to drop unit 576 from 0 th HD out of 76 possible.  usage down to 1.0524360122606362\n",
      "all done trying to reduce usage of unit 576 final usage = 1.01548 212 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01256 0.15142\n",
      "starting to reduce usage for unit 444 with usage 1.05171 . Total units tried = 137\n",
      "try to drop unit 444 from 0 th HD out of 80 possible.  usage down to 1.051706062859576\n",
      "all done trying to reduce usage of unit 444 final usage = 1.01567 213 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01254 0.1514\n",
      "starting to reduce usage for unit 3100 with usage 1.05053 . Total units tried = 138\n",
      "try to drop unit 3100 from 0 th HD out of 78 possible.  usage down to 1.0505314316395507\n",
      "all done trying to reduce usage of unit 3100 final usage = 1.01296 213 sec elapsed 3 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01253 0.15138\n",
      "starting to reduce usage for unit 3029 with usage 1.04947 . Total units tried = 139\n",
      "try to drop unit 3029 from 0 th HD out of 98 possible.  usage down to 1.0494658733185165\n",
      "all done trying to reduce usage of unit 3029 final usage = 1.01591 213 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01252 0.15135\n",
      "starting to reduce usage for unit 856 with usage 1.04849 . Total units tried = 140\n",
      "try to drop unit 856 from 0 th HD out of 101 possible.  usage down to 1.0484926074505319\n",
      "all done trying to reduce usage of unit 856 final usage = 1.01514 213 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01251 0.15134\n",
      "starting to reduce usage for unit 2112 with usage 1.04827 . Total units tried = 141\n",
      "try to drop unit 2112 from 0 th HD out of 113 possible.  usage down to 1.0482744616526096\n",
      "all done trying to reduce usage of unit 2112 final usage = 1.01586 214 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01248 0.15131\n",
      "starting to reduce usage for unit 528 with usage 1.04809 . Total units tried = 142\n",
      "try to drop unit 528 from 0 th HD out of 95 possible.  usage down to 1.048089876746499\n",
      "all done trying to reduce usage of unit 528 final usage = 1.01764 214 sec elapsed 2 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.01246 0.15131\n",
      "starting to reduce usage for unit 1260 with usage 1.04771 . Total units tried = 143\n",
      "try to drop unit 1260 from 0 th HD out of 87 possible.  usage down to 1.0477123167114981\n",
      "all done trying to reduce usage of unit 1260 final usage = 1.00728 214 sec elapsed 3 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01245 0.15129\n",
      "starting to reduce usage for unit 392 with usage 1.04754 . Total units tried = 144\n",
      "try to drop unit 392 from 0 th HD out of 89 possible.  usage down to 1.047536122028476\n",
      "all done trying to reduce usage of unit 392 final usage = 1.01745 214 sec elapsed 2 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01243 0.15127\n",
      "starting to reduce usage for unit 609 with usage 1.04716 . Total units tried = 145\n",
      "try to drop unit 609 from 0 th HD out of 66 possible.  usage down to 1.047158561993464\n",
      "all done trying to reduce usage of unit 609 final usage = 1.01783 214 sec elapsed 2 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01243 0.15125\n",
      "starting to reduce usage for unit 1824 with usage 1.04688 . Total units tried = 146\n",
      "try to drop unit 1824 from 0 th HD out of 82 possible.  usage down to 1.0468816846345126\n",
      "all done trying to reduce usage of unit 1824 final usage = 1.01718 214 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01241 0.15123\n",
      "starting to reduce usage for unit 374 with usage 1.04629 . Total units tried = 147\n",
      "try to drop unit 374 from 0 th HD out of 83 possible.  usage down to 1.0462943690243482\n",
      "all done trying to reduce usage of unit 374 final usage = 1.0133 215 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01239 0.15119\n",
      "starting to reduce usage for unit 2835 with usage 1.04589 . Total units tried = 148\n",
      "try to drop unit 2835 from 0 th HD out of 85 possible.  usage down to 1.0458916383204162\n",
      "all done trying to reduce usage of unit 2835 final usage = 1.01405 215 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01237 0.15119\n",
      "starting to reduce usage for unit 658 with usage 1.04588 . Total units tried = 149\n",
      "try to drop unit 658 from 0 th HD out of 76 possible.  usage down to 1.045883248097426\n",
      "all done trying to reduce usage of unit 658 final usage = 1.01239 215 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01235 0.15117\n",
      "starting to reduce usage for unit 878 with usage 1.04477 . Total units tried = 150\n",
      "try to drop unit 878 from 0 th HD out of 73 possible.  usage down to 1.0447673484383515\n",
      "all done trying to reduce usage of unit 878 final usage = 1.01438 215 sec elapsed 5 2 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01234 0.15117\n",
      "starting to reduce usage for unit 685 with usage 1.04426 . Total units tried = 151\n",
      "try to drop unit 685 from 0 th HD out of 75 possible.  usage down to 1.044255544835384\n",
      "all done trying to reduce usage of unit 685 final usage = 1.01779 215 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01233 0.15116\n",
      "starting to reduce usage for unit 383 with usage 1.04336 . Total units tried = 152\n",
      "try to drop unit 383 from 0 th HD out of 80 possible.  usage down to 1.0433577909742533\n",
      "all done trying to reduce usage of unit 383 final usage = 1.0158 216 sec elapsed 2 5 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01232 0.15115\n",
      "starting to reduce usage for unit 1576 with usage 1.04324 . Total units tried = 153\n",
      "try to drop unit 1576 from 0 th HD out of 80 possible.  usage down to 1.0432403278522988\n",
      "all done trying to reduce usage of unit 1576 final usage = 1.01122 216 sec elapsed 2 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.01229 0.15111\n",
      "starting to reduce usage for unit 2456 with usage 1.0432 . Total units tried = 154\n",
      "try to drop unit 2456 from 0 th HD out of 79 possible.  usage down to 1.0431983767373585\n",
      "all done trying to reduce usage of unit 2456 final usage = 1.01339 216 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0123 0.1511\n",
      "starting to reduce usage for unit 1864 with usage 1.04257 . Total units tried = 155\n",
      "try to drop unit 1864 from 0 th HD out of 92 possible.  usage down to 1.0425691100123422\n",
      "all done trying to reduce usage of unit 1864 final usage = 1.0101 217 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01227 0.15108\n",
      "starting to reduce usage for unit 3028 with usage 1.04227 . Total units tried = 156\n",
      "try to drop unit 3028 from 0 th HD out of 98 possible.  usage down to 1.0422670619843968\n",
      "all done trying to reduce usage of unit 3028 final usage = 1.01803 217 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01225 0.15106\n",
      "starting to reduce usage for unit 2270 with usage 1.04212 . Total units tried = 157\n",
      "try to drop unit 2270 from 0 th HD out of 77 possible.  usage down to 1.0421160379703502\n",
      "all done trying to reduce usage of unit 2270 final usage = 1.01336 217 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01224 0.15105\n",
      "starting to reduce usage for unit 147 with usage 1.04141 . Total units tried = 158\n",
      "try to drop unit 147 from 0 th HD out of 79 possible.  usage down to 1.0414112592381934\n",
      "all done trying to reduce usage of unit 147 final usage = 1.01293 217 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01222 0.15103\n",
      "starting to reduce usage for unit 2157 with usage 1.04063 . Total units tried = 159\n",
      "try to drop unit 2157 from 0 th HD out of 82 possible.  usage down to 1.0406309684992563\n",
      "all done trying to reduce usage of unit 2157 final usage = 1.0194 217 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01221 0.15099\n",
      "starting to reduce usage for unit 2212 with usage 1.0403 . Total units tried = 160\n",
      "try to drop unit 2212 from 0 th HD out of 92 possible.  usage down to 1.0402953595792648\n",
      "all done trying to reduce usage of unit 2212 final usage = 1.01155 217 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01221 0.15095\n",
      "starting to reduce usage for unit 275 with usage 1.04025 . Total units tried = 161\n",
      "try to drop unit 275 from 0 th HD out of 100 possible.  usage down to 1.0402450182412437\n",
      "all done trying to reduce usage of unit 275 final usage = 1.01783 217 sec elapsed 3 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01221 0.15095\n",
      "starting to reduce usage for unit 1688 with usage 1.04005 . Total units tried = 162\n",
      "try to drop unit 1688 from 0 th HD out of 82 possible.  usage down to 1.040052043112252\n",
      "all done trying to reduce usage of unit 1688 final usage = 1.0107 218 sec elapsed 2 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.0122 0.15094\n",
      "starting to reduce usage for unit 743 with usage 1.03968 . Total units tried = 163\n",
      "try to drop unit 743 from 0 th HD out of 94 possible.  usage down to 1.0396828733003032\n",
      "all done trying to reduce usage of unit 743 final usage = 1.01575 218 sec elapsed 2 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01219 0.15093\n",
      "starting to reduce usage for unit 2433 with usage 1.03962 . Total units tried = 164\n",
      "try to drop unit 2433 from 0 th HD out of 78 possible.  usage down to 1.0396241417391625\n",
      "all done trying to reduce usage of unit 2433 final usage = 1.00845 218 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01216 0.1509\n",
      "starting to reduce usage for unit 890 with usage 1.03895 . Total units tried = 165\n",
      "try to drop unit 890 from 0 th HD out of 80 possible.  usage down to 1.038952923899217\n",
      "all done trying to reduce usage of unit 890 final usage = 1.0148 218 sec elapsed 3 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01215 0.15088\n",
      "starting to reduce usage for unit 1385 with usage 1.03873 . Total units tried = 166\n",
      "try to drop unit 1385 from 0 th HD out of 79 possible.  usage down to 1.0387263878781752\n",
      "all done trying to reduce usage of unit 1385 final usage = 1.01238 219 sec elapsed 3 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.01214 0.15085\n",
      "starting to reduce usage for unit 3119 with usage 1.03861 . Total units tried = 167\n",
      "try to drop unit 3119 from 0 th HD out of 76 possible.  usage down to 1.038608924756142\n",
      "all done trying to reduce usage of unit 3119 final usage = 1.01007 219 sec elapsed 2 0 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.01214 0.15082\n",
      "starting to reduce usage for unit 2919 with usage 1.03838 . Total units tried = 168\n",
      "try to drop unit 2919 from 0 th HD out of 71 possible.  usage down to 1.0383823887352008\n",
      "all done trying to reduce usage of unit 2919 final usage = 1.00962 219 sec elapsed 2 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01211 0.15082\n",
      "starting to reduce usage for unit 1556 with usage 1.03835 . Total units tried = 169\n",
      "try to drop unit 1556 from 0 th HD out of 118 possible.  usage down to 1.0383488278432866\n",
      "all done trying to reduce usage of unit 1556 final usage = 1.0148 219 sec elapsed 3 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.01211 0.15082\n",
      "starting to reduce usage for unit 2063 with usage 1.03817 . Total units tried = 170\n",
      "try to drop unit 2063 from 0 th HD out of 83 possible.  usage down to 1.0381726331600856\n",
      "all done trying to reduce usage of unit 2063 final usage = 1.01721 219 sec elapsed 2 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.0121 0.1508\n",
      "starting to reduce usage for unit 2047 with usage 1.03792 . Total units tried = 171\n",
      "try to drop unit 2047 from 0 th HD out of 50 possible.  usage down to 1.0379209264701403\n",
      "all done trying to reduce usage of unit 2047 final usage = 1.01112 220 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0121 0.15078\n",
      "starting to reduce usage for unit 448 with usage 1.03621 . Total units tried = 172\n",
      "try to drop unit 448 from 0 th HD out of 72 possible.  usage down to 1.0362093209780845\n",
      "all done trying to reduce usage of unit 448 final usage = 1.00953 220 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01209 0.15077\n",
      "starting to reduce usage for unit 786 with usage 1.03593 . Total units tried = 173\n",
      "try to drop unit 786 from 0 th HD out of 72 possible.  usage down to 1.0359324436190884\n",
      "all done trying to reduce usage of unit 786 final usage = 1.0124 221 sec elapsed 2 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01209 0.15077\n",
      "starting to reduce usage for unit 907 with usage 1.03549 . Total units tried = 174\n",
      "try to drop unit 907 from 0 th HD out of 93 possible.  usage down to 1.0354877618001865\n",
      "all done trying to reduce usage of unit 907 final usage = 1.01266 221 sec elapsed 2 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01208 0.15074\n",
      "starting to reduce usage for unit 2625 with usage 1.03496 . Total units tried = 175\n",
      "try to drop unit 2625 from 0 th HD out of 74 possible.  usage down to 1.0349591777510825\n",
      "all done trying to reduce usage of unit 2625 final usage = 1.01537 221 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01207 0.15074\n",
      "starting to reduce usage for unit 169 with usage 1.03495 . Total units tried = 176\n",
      "try to drop unit 169 from 0 th HD out of 102 possible.  usage down to 1.0349507875280428\n",
      "all done trying to reduce usage of unit 169 final usage = 1.01245 222 sec elapsed 2 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01205 0.15072\n",
      "starting to reduce usage for unit 2598 with usage 1.03493 . Total units tried = 177\n",
      "try to drop unit 2598 from 0 th HD out of 92 possible.  usage down to 1.0349256168590937\n",
      "all done trying to reduce usage of unit 2598 final usage = 1.01584 222 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01204 0.15072\n",
      "starting to reduce usage for unit 399 with usage 1.03483 . Total units tried = 178\n",
      "try to drop unit 399 from 0 th HD out of 79 possible.  usage down to 1.0348333244061183\n",
      "all done trying to reduce usage of unit 399 final usage = 1.01099 222 sec elapsed 2 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01202 0.1507\n",
      "starting to reduce usage for unit 2211 with usage 1.03456 . Total units tried = 179\n",
      "try to drop unit 2211 from 0 th HD out of 73 possible.  usage down to 1.0345564470470259\n",
      "all done trying to reduce usage of unit 2211 final usage = 1.01921 222 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01203 0.15069\n",
      "starting to reduce usage for unit 656 with usage 1.03392 . Total units tried = 180\n",
      "try to drop unit 656 from 0 th HD out of 100 possible.  usage down to 1.033918790099067\n",
      "all done trying to reduce usage of unit 656 final usage = 1.01413 223 sec elapsed 2 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.01202 0.15069\n",
      "starting to reduce usage for unit 2260 with usage 1.03343 . Total units tried = 181\n",
      "try to drop unit 2260 from 0 th HD out of 80 possible.  usage down to 1.0334321571650134\n",
      "all done trying to reduce usage of unit 2260 final usage = 1.01926 223 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01202 0.15067\n",
      "starting to reduce usage for unit 532 with usage 1.03293 . Total units tried = 182\n",
      "try to drop unit 532 from 0 th HD out of 78 possible.  usage down to 1.032928743784986\n",
      "all done trying to reduce usage of unit 532 final usage = 1.00861 223 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01201 0.15067\n",
      "starting to reduce usage for unit 1379 with usage 1.03217 . Total units tried = 183\n",
      "try to drop unit 1379 from 0 th HD out of 76 possible.  usage down to 1.032173623714991\n",
      "all done trying to reduce usage of unit 1379 final usage = 1.01663 223 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.012 0.15067\n",
      "starting to reduce usage for unit 593 with usage 1.03175 . Total units tried = 184\n",
      "try to drop unit 593 from 0 th HD out of 107 possible.  usage down to 1.0317457223420377\n",
      "all done trying to reduce usage of unit 593 final usage = 1.01076 223 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01199 0.15066\n",
      "starting to reduce usage for unit 1268 with usage 1.03143 . Total units tried = 185\n",
      "try to drop unit 1268 from 0 th HD out of 97 possible.  usage down to 1.0314268938679654\n",
      "all done trying to reduce usage of unit 1268 final usage = 1.01541 223 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01198 0.15065\n",
      "starting to reduce usage for unit 458 with usage 1.03072 . Total units tried = 186\n",
      "try to drop unit 458 from 0 th HD out of 78 possible.  usage down to 1.0307221151359471\n",
      "all done trying to reduce usage of unit 458 final usage = 1.00174 224 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01196 0.15065\n",
      "starting to reduce usage for unit 483 with usage 1.03045 . Total units tried = 187\n",
      "try to drop unit 483 from 0 th HD out of 73 possible.  usage down to 1.030453627999966\n",
      "all done trying to reduce usage of unit 483 final usage = 1.00854 224 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01194 0.15064\n",
      "starting to reduce usage for unit 1693 with usage 1.03024 . Total units tried = 188\n",
      "try to drop unit 1693 from 0 th HD out of 86 possible.  usage down to 1.0302354822020148\n",
      "all done trying to reduce usage of unit 1693 final usage = 1.01484 224 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01193 0.15063\n",
      "starting to reduce usage for unit 2204 with usage 1.03017 . Total units tried = 189\n",
      "try to drop unit 2204 from 0 th HD out of 102 possible.  usage down to 1.0301683604179652\n",
      "all done trying to reduce usage of unit 2204 final usage = 1.01384 224 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01191 0.15063\n",
      "starting to reduce usage for unit 1627 with usage 1.02982 . Total units tried = 190\n",
      "try to drop unit 1627 from 0 th HD out of 72 possible.  usage down to 1.0298243612749\n",
      "all done trying to reduce usage of unit 1627 final usage = 1.00837 224 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01191 0.15059\n",
      "starting to reduce usage for unit 3092 with usage 1.02976 . Total units tried = 191\n",
      "try to drop unit 3092 from 0 th HD out of 74 possible.  usage down to 1.0297572394908365\n",
      "all done trying to reduce usage of unit 3092 final usage = 1.01903 224 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01191 0.15059\n",
      "starting to reduce usage for unit 2309 with usage 1.02939 . Total units tried = 192\n",
      "try to drop unit 2309 from 0 th HD out of 133 possible.  usage down to 1.0293880696790334\n",
      "all done trying to reduce usage of unit 2309 final usage = 1.01804 225 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0119 0.15058\n",
      "starting to reduce usage for unit 2185 with usage 1.02935 . Total units tried = 193\n",
      "try to drop unit 2185 from 0 th HD out of 97 possible.  usage down to 1.0293461185639337\n",
      "all done trying to reduce usage of unit 2185 final usage = 1.00853 225 sec elapsed 2 1 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.01189 0.15058\n",
      "starting to reduce usage for unit 510 with usage 1.02893 . Total units tried = 194\n",
      "try to drop unit 510 from 0 th HD out of 82 possible.  usage down to 1.0289349976369735\n",
      "all done trying to reduce usage of unit 510 final usage = 1.00334 225 sec elapsed 2 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01187 0.15057\n",
      "starting to reduce usage for unit 934 with usage 1.02867 . Total units tried = 195\n",
      "try to drop unit 934 from 0 th HD out of 84 possible.  usage down to 1.0286749007238987\n",
      "all done trying to reduce usage of unit 934 final usage = 1.00881 225 sec elapsed 1 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01186 0.15056\n",
      "starting to reduce usage for unit 1199 with usage 1.02865 . Total units tried = 196\n",
      "try to drop unit 1199 from 0 th HD out of 79 possible.  usage down to 1.028649730054882\n",
      "all done trying to reduce usage of unit 1199 final usage = 1.01684 225 sec elapsed 1 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01186 0.15056\n",
      "starting to reduce usage for unit 2122 with usage 1.02852 . Total units tried = 197\n",
      "try to drop unit 2122 from 0 th HD out of 73 possible.  usage down to 1.0285154864868806\n",
      "all done trying to reduce usage of unit 2122 final usage = 1.01871 225 sec elapsed 1 6 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01185 0.15055\n",
      "starting to reduce usage for unit 548 with usage 1.02849 . Total units tried = 198\n",
      "try to drop unit 548 from 0 th HD out of 77 possible.  usage down to 1.0284903158178633\n",
      "all done trying to reduce usage of unit 548 final usage = 1.01014 225 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01185 0.1505\n",
      "starting to reduce usage for unit 1740 with usage 1.02778 . Total units tried = 199\n",
      "try to drop unit 1740 from 0 th HD out of 83 possible.  usage down to 1.027777146862894\n",
      "all done trying to reduce usage of unit 1740 final usage = 1.01633 226 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01184 0.1505\n",
      "starting to reduce usage for unit 939 with usage 1.02763 . Total units tried = 200\n",
      "try to drop unit 939 from 0 th HD out of 69 possible.  usage down to 1.0276345130718212\n",
      "all done trying to reduce usage of unit 939 final usage = 1.01063 226 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01183 0.15049\n",
      "starting to reduce usage for unit 9 with usage 1.02708 . Total units tried = 201\n",
      "try to drop unit 9 from 0 th HD out of 56 possible.  usage down to 1.027080758353865\n",
      "all done trying to reduce usage of unit 9 final usage = 1.01774 226 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01183 0.15049\n",
      "starting to reduce usage for unit 935 with usage 1.02701 . Total units tried = 202\n",
      "try to drop unit 935 from 0 th HD out of 81 possible.  usage down to 1.0270136365698184\n",
      "all done trying to reduce usage of unit 935 final usage = 1.01307 226 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01182 0.15049\n",
      "starting to reduce usage for unit 763 with usage 1.02695 . Total units tried = 203\n",
      "try to drop unit 763 from 0 th HD out of 65 possible.  usage down to 1.0269465147858698\n",
      "all done trying to reduce usage of unit 763 final usage = 1.01073 226 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01181 0.15048\n",
      "starting to reduce usage for unit 415 with usage 1.02689 . Total units tried = 204\n",
      "try to drop unit 415 from 0 th HD out of 78 possible.  usage down to 1.0268877832248635\n",
      "all done trying to reduce usage of unit 415 final usage = 1.00719 226 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0118 0.15047\n",
      "starting to reduce usage for unit 896 with usage 1.02794 . Total units tried = 205\n",
      "try to drop unit 896 from 0 th HD out of 71 possible.  usage down to 1.0279365610998672\n",
      "all done trying to reduce usage of unit 896 final usage = 1.01597 227 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0118 0.15047\n",
      "starting to reduce usage for unit 611 with usage 1.02652 . Total units tried = 206\n",
      "try to drop unit 611 from 0 th HD out of 88 possible.  usage down to 1.0265186134126778\n",
      "all done trying to reduce usage of unit 611 final usage = 1.01169 227 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01179 0.15045\n",
      "starting to reduce usage for unit 2305 with usage 1.02648 . Total units tried = 207\n",
      "try to drop unit 2305 from 0 th HD out of 134 possible.  usage down to 1.0264766622979369\n",
      "all done trying to reduce usage of unit 2305 final usage = 1.0009 227 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01176 0.15044\n",
      "starting to reduce usage for unit 779 with usage 1.02643 . Total units tried = 208\n",
      "try to drop unit 779 from 0 th HD out of 91 possible.  usage down to 1.0264347111827907\n",
      "all done trying to reduce usage of unit 779 final usage = 1.00455 227 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01176 0.15043\n",
      "starting to reduce usage for unit 3072 with usage 1.02626 . Total units tried = 209\n",
      "try to drop unit 3072 from 0 th HD out of 84 possible.  usage down to 1.0262585164997795\n",
      "all done trying to reduce usage of unit 3072 final usage = 1.0197 227 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01177 0.15043\n",
      "starting to reduce usage for unit 1328 with usage 1.02624 . Total units tried = 210\n",
      "try to drop unit 1328 from 0 th HD out of 78 possible.  usage down to 1.0262417360538245\n",
      "all done trying to reduce usage of unit 1328 final usage = 1.01753 227 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01176 0.15043\n",
      "starting to reduce usage for unit 2542 with usage 1.02613 . Total units tried = 211\n",
      "try to drop unit 2542 from 0 th HD out of 64 possible.  usage down to 1.0261326631548395\n",
      "all done trying to reduce usage of unit 2542 final usage = 1.01875 228 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01176 0.15043\n",
      "starting to reduce usage for unit 2636 with usage 1.02567 . Total units tried = 212\n",
      "try to drop unit 2636 from 0 th HD out of 81 possible.  usage down to 1.0256712008897555\n",
      "all done trying to reduce usage of unit 2636 final usage = 1.01344 228 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01175 0.15042\n",
      "starting to reduce usage for unit 231 with usage 1.02564 . Total units tried = 213\n",
      "try to drop unit 231 from 0 th HD out of 88 possible.  usage down to 1.025637639997783\n",
      "all done trying to reduce usage of unit 231 final usage = 1.01392 228 sec elapsed 2 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01174 0.15042\n",
      "starting to reduce usage for unit 2233 with usage 1.0256 . Total units tried = 214\n",
      "try to drop unit 2233 from 0 th HD out of 65 possible.  usage down to 1.0256040791057746\n",
      "all done trying to reduce usage of unit 2233 final usage = 1.01081 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01173 0.15041\n",
      "starting to reduce usage for unit 1259 with usage 1.0256 . Total units tried = 215\n",
      "try to drop unit 1259 from 0 th HD out of 90 possible.  usage down to 1.0255956888827922\n",
      "all done trying to reduce usage of unit 1259 final usage = 1.0187 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01173 0.1504\n",
      "starting to reduce usage for unit 2964 with usage 1.02529 . Total units tried = 216\n",
      "try to drop unit 2964 from 0 th HD out of 69 possible.  usage down to 1.0252852506317862\n",
      "all done trying to reduce usage of unit 2964 final usage = 1.01408 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01172 0.1504\n",
      "starting to reduce usage for unit 1784 with usage 1.02527 . Total units tried = 217\n",
      "try to drop unit 1784 from 0 th HD out of 95 possible.  usage down to 1.0252684701857382\n",
      "all done trying to reduce usage of unit 1784 final usage = 1.01336 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01171 0.1504\n",
      "starting to reduce usage for unit 2808 with usage 1.02508 . Total units tried = 218\n",
      "try to drop unit 2808 from 0 th HD out of 81 possible.  usage down to 1.0250754950567893\n",
      "all done trying to reduce usage of unit 2808 final usage = 1.01623 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01171 0.1504\n",
      "starting to reduce usage for unit 2687 with usage 1.02506 . Total units tried = 219\n",
      "try to drop unit 2687 from 0 th HD out of 78 possible.  usage down to 1.0250587146107257\n",
      "all done trying to reduce usage of unit 2687 final usage = 1.01065 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0117 0.15039\n",
      "starting to reduce usage for unit 1236 with usage 1.02497 . Total units tried = 220\n",
      "try to drop unit 1236 from 0 th HD out of 70 possible.  usage down to 1.024966422157737\n",
      "all done trying to reduce usage of unit 1236 final usage = 1.01778 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01169 0.15039\n",
      "starting to reduce usage for unit 2084 with usage 1.02487 . Total units tried = 221\n",
      "try to drop unit 2084 from 0 th HD out of 95 possible.  usage down to 1.0248657394818328\n",
      "all done trying to reduce usage of unit 2084 final usage = 1.01216 229 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01168 0.15038\n",
      "starting to reduce usage for unit 1197 with usage 1.0248 . Total units tried = 222\n",
      "try to drop unit 1197 from 0 th HD out of 77 possible.  usage down to 1.0247986176977582\n",
      "all done trying to reduce usage of unit 1197 final usage = 1.01532 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01168 0.15038\n",
      "starting to reduce usage for unit 2005 with usage 1.02476 . Total units tried = 223\n",
      "try to drop unit 2005 from 0 th HD out of 86 possible.  usage down to 1.0247566665826484\n",
      "all done trying to reduce usage of unit 2005 final usage = 1.01135 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01167 0.15038\n",
      "starting to reduce usage for unit 2267 with usage 1.02457 . Total units tried = 224\n",
      "try to drop unit 2267 from 0 th HD out of 85 possible.  usage down to 1.0245720816767474\n",
      "all done trying to reduce usage of unit 2267 final usage = 1.01163 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01166 0.15037\n",
      "starting to reduce usage for unit 645 with usage 1.02455 . Total units tried = 225\n",
      "try to drop unit 645 from 0 th HD out of 86 possible.  usage down to 1.0245469110077332\n",
      "all done trying to reduce usage of unit 645 final usage = 1.00904 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01165 0.15036\n",
      "starting to reduce usage for unit 795 with usage 1.02435 . Total units tried = 226\n",
      "try to drop unit 795 from 0 th HD out of 84 possible.  usage down to 1.0243455456557293\n",
      "all done trying to reduce usage of unit 795 final usage = 1.00772 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01164 0.15036\n",
      "starting to reduce usage for unit 2057 with usage 1.02616 . Total units tried = 227\n",
      "try to drop unit 2057 from 0 th HD out of 84 possible.  usage down to 1.0261578338237793\n",
      "all done trying to reduce usage of unit 2057 final usage = 1.01652 230 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01163 0.15036\n",
      "starting to reduce usage for unit 2269 with usage 1.02402 . Total units tried = 228\n",
      "try to drop unit 2269 from 0 th HD out of 71 possible.  usage down to 1.0240183269586944\n",
      "all done trying to reduce usage of unit 2269 final usage = 1.01671 231 sec elapsed 1 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01162 0.15036\n",
      "starting to reduce usage for unit 879 with usage 1.02393 . Total units tried = 229\n",
      "try to drop unit 879 from 0 th HD out of 82 possible.  usage down to 1.0239344247287374\n",
      "all done trying to reduce usage of unit 879 final usage = 0.99882 231 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01162 0.15036\n",
      "starting to reduce usage for unit 3062 with usage 1.02795 . Total units tried = 230\n",
      "try to drop unit 3062 from 0 th HD out of 84 possible.  usage down to 1.0279533415459166\n",
      "all done trying to reduce usage of unit 3062 final usage = 1.01877 231 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01162 0.15036\n",
      "starting to reduce usage for unit 428 with usage 1.02683 . Total units tried = 231\n",
      "try to drop unit 428 from 0 th HD out of 70 possible.  usage down to 1.0268290516638887\n",
      "all done trying to reduce usage of unit 428 final usage = 1.01467 231 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01161 0.15036\n",
      "starting to reduce usage for unit 3145 with usage 1.0237 . Total units tried = 232\n",
      "try to drop unit 3145 from 0 th HD out of 84 possible.  usage down to 1.023699498484721\n",
      "all done trying to reduce usage of unit 3145 final usage = 1.01528 231 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01161 0.15036\n",
      "starting to reduce usage for unit 901 with usage 1.02369 . Total units tried = 233\n",
      "try to drop unit 901 from 0 th HD out of 91 possible.  usage down to 1.0236911082617526\n",
      "all done trying to reduce usage of unit 901 final usage = 0.99544 231 sec elapsed 1 0 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.01162 0.15031\n",
      "starting to reduce usage for unit 2515 with usage 1.03326 . Total units tried = 234\n",
      "try to drop unit 2515 from 0 th HD out of 91 possible.  usage down to 1.0332643527050183\n",
      "all done trying to reduce usage of unit 2515 final usage = 1.01425 232 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0116 0.15031\n",
      "starting to reduce usage for unit 166 with usage 1.02731 . Total units tried = 235\n",
      "try to drop unit 166 from 0 th HD out of 86 possible.  usage down to 1.0273072943748291\n",
      "all done trying to reduce usage of unit 166 final usage = 1.01702 232 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01159 0.15031\n",
      "starting to reduce usage for unit 2505 with usage 1.02345 . Total units tried = 236\n",
      "try to drop unit 2505 from 0 th HD out of 86 possible.  usage down to 1.0234477917947198\n",
      "all done trying to reduce usage of unit 2505 final usage = 1.00496 232 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01159 0.15031\n",
      "starting to reduce usage for unit 1401 with usage 1.02344 . Total units tried = 237\n",
      "try to drop unit 1401 from 0 th HD out of 75 possible.  usage down to 1.0234394015716783\n",
      "all done trying to reduce usage of unit 1401 final usage = 1.01153 232 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01157 0.1503\n",
      "starting to reduce usage for unit 3051 with usage 1.02342 . Total units tried = 238\n",
      "try to drop unit 3051 from 0 th HD out of 77 possible.  usage down to 1.0234226211256854\n",
      "all done trying to reduce usage of unit 3051 final usage = 1.01071 232 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01156 0.1503\n",
      "starting to reduce usage for unit 798 with usage 1.02342 . Total units tried = 239\n",
      "try to drop unit 798 from 0 th HD out of 66 possible.  usage down to 1.0234226211256445\n",
      "all done trying to reduce usage of unit 798 final usage = 1.01004 232 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01155 0.1503\n",
      "starting to reduce usage for unit 2551 with usage 1.02337 . Total units tried = 240\n",
      "try to drop unit 2551 from 0 th HD out of 79 possible.  usage down to 1.0233722797877263\n",
      "all done trying to reduce usage of unit 2551 final usage = 0.9985 233 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01156 0.1503\n",
      "starting to reduce usage for unit 2143 with usage 1.03035 . Total units tried = 241\n",
      "try to drop unit 2143 from 0 th HD out of 77 possible.  usage down to 1.0303529453239242\n",
      "all done trying to reduce usage of unit 2143 final usage = 1.01127 233 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01155 0.15029\n",
      "starting to reduce usage for unit 841 with usage 1.02444 . Total units tried = 242\n",
      "try to drop unit 841 from 0 th HD out of 90 possible.  usage down to 1.0244378381087627\n",
      "all done trying to reduce usage of unit 841 final usage = 1.01366 233 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01155 0.15029\n",
      "starting to reduce usage for unit 753 with usage 1.02338 . Total units tried = 243\n",
      "try to drop unit 753 from 0 th HD out of 83 possible.  usage down to 1.0233806700107286\n",
      "all done trying to reduce usage of unit 753 final usage = 1.01446 233 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01154 0.15029\n",
      "starting to reduce usage for unit 565 with usage 1.0233 . Total units tried = 244\n",
      "try to drop unit 565 from 0 th HD out of 85 possible.  usage down to 1.0232967677806761\n",
      "all done trying to reduce usage of unit 565 final usage = 1.0119 233 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01153 0.15029\n",
      "starting to reduce usage for unit 1380 with usage 1.02329 . Total units tried = 245\n",
      "try to drop unit 1380 from 0 th HD out of 79 possible.  usage down to 1.0232883775577273\n",
      "all done trying to reduce usage of unit 1380 final usage = 1.00837 233 sec elapsed 1 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.01153 0.15029\n",
      "starting to reduce usage for unit 2896 with usage 1.02326 . Total units tried = 246\n",
      "try to drop unit 2896 from 0 th HD out of 79 possible.  usage down to 1.0232632068886915\n",
      "all done trying to reduce usage of unit 2896 final usage = 1.0098 234 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01152 0.15029\n",
      "starting to reduce usage for unit 1398 with usage 1.02316 . Total units tried = 247\n",
      "try to drop unit 1398 from 0 th HD out of 104 possible.  usage down to 1.0231625242127567\n",
      "all done trying to reduce usage of unit 1398 final usage = 1.01094 234 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01152 0.15029\n",
      "starting to reduce usage for unit 1044 with usage 1.02304 . Total units tried = 248\n",
      "try to drop unit 1044 from 0 th HD out of 89 possible.  usage down to 1.0230366708677965\n",
      "all done trying to reduce usage of unit 1044 final usage = 1.00996 234 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01151 0.15027\n",
      "starting to reduce usage for unit 2354 with usage 1.02304 . Total units tried = 249\n",
      "try to drop unit 2354 from 0 th HD out of 104 possible.  usage down to 1.0230366708677823\n",
      "all done trying to reduce usage of unit 2354 final usage = 1.01432 234 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01151 0.15026\n",
      "starting to reduce usage for unit 325 with usage 1.02297 . Total units tried = 250\n",
      "try to drop unit 325 from 0 th HD out of 86 possible.  usage down to 1.0229695490835764\n",
      "all done trying to reduce usage of unit 325 final usage = 1.01022 234 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0115 0.15025\n",
      "starting to reduce usage for unit 2664 with usage 1.02295 . Total units tried = 251\n",
      "try to drop unit 2664 from 0 th HD out of 102 possible.  usage down to 1.0229527686377324\n",
      "all done trying to reduce usage of unit 2664 final usage = 1.01377 235 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0115 0.15025\n",
      "starting to reduce usage for unit 464 with usage 1.02291 . Total units tried = 252\n",
      "try to drop unit 464 from 0 th HD out of 86 possible.  usage down to 1.022910817522761\n",
      "all done trying to reduce usage of unit 464 final usage = 1.00748 235 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01149 0.15023\n",
      "starting to reduce usage for unit 2184 with usage 1.02275 . Total units tried = 253\n",
      "try to drop unit 2184 from 0 th HD out of 66 possible.  usage down to 1.0227514032857357\n",
      "all done trying to reduce usage of unit 2184 final usage = 1.01125 235 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01149 0.15019\n",
      "starting to reduce usage for unit 470 with usage 1.0224 . Total units tried = 254\n",
      "try to drop unit 470 from 0 th HD out of 83 possible.  usage down to 1.0223990139197217\n",
      "all done trying to reduce usage of unit 470 final usage = 1.01409 235 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01148 0.15019\n",
      "starting to reduce usage for unit 2038 with usage 1.02226 . Total units tried = 255\n",
      "try to drop unit 2038 from 0 th HD out of 65 possible.  usage down to 1.0222563801286517\n",
      "all done trying to reduce usage of unit 2038 final usage = 1.00958 236 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01148 0.15019\n",
      "starting to reduce usage for unit 911 with usage 1.02212 . Total units tried = 256\n",
      "try to drop unit 911 from 0 th HD out of 87 possible.  usage down to 1.0221221365607132\n",
      "all done trying to reduce usage of unit 911 final usage = 1.00149 236 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01148 0.15019\n",
      "starting to reduce usage for unit 1739 with usage 1.02445 . Total units tried = 257\n",
      "try to drop unit 1739 from 0 th HD out of 74 possible.  usage down to 1.0244546185547452\n",
      "all done trying to reduce usage of unit 1739 final usage = 1.00954 236 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01148 0.15018\n",
      "starting to reduce usage for unit 948 with usage 1.02421 . Total units tried = 258\n",
      "try to drop unit 948 from 0 th HD out of 77 possible.  usage down to 1.0242113020877703\n",
      "all done trying to reduce usage of unit 948 final usage = 1.01312 236 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01148 0.15018\n",
      "starting to reduce usage for unit 1253 with usage 1.02205 . Total units tried = 259\n",
      "try to drop unit 1253 from 0 th HD out of 80 possible.  usage down to 1.0220466245536326\n",
      "all done trying to reduce usage of unit 1253 final usage = 1.01589 236 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01148 0.15017\n",
      "starting to reduce usage for unit 1367 with usage 1.022 . Total units tried = 260\n",
      "try to drop unit 1367 from 0 th HD out of 108 possible.  usage down to 1.0219962832157876\n",
      "all done trying to reduce usage of unit 1367 final usage = 1.01286 236 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01148 0.15016\n",
      "starting to reduce usage for unit 771 with usage 1.02198 . Total units tried = 261\n",
      "try to drop unit 771 from 0 th HD out of 76 possible.  usage down to 1.0219795027696468\n",
      "all done trying to reduce usage of unit 771 final usage = 1.01084 236 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01147 0.15015\n",
      "starting to reduce usage for unit 2201 with usage 1.02173 . Total units tried = 262\n",
      "try to drop unit 2201 from 0 th HD out of 81 possible.  usage down to 1.0217277960795772\n",
      "all done trying to reduce usage of unit 2201 final usage = 1.00781 237 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01147 0.15015\n",
      "starting to reduce usage for unit 1303 with usage 1.02285 . Total units tried = 263\n",
      "try to drop unit 1303 from 0 th HD out of 75 possible.  usage down to 1.0228520859617147\n",
      "all done trying to reduce usage of unit 1303 final usage = 0.99766 237 sec elapsed 1 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01147 0.15014\n",
      "starting to reduce usage for unit 2534 with usage 1.03391 . Total units tried = 264\n",
      "try to drop unit 2534 from 0 th HD out of 79 possible.  usage down to 1.0339103998760804\n",
      "all done trying to reduce usage of unit 2534 final usage = 1.00981 237 sec elapsed 2 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01146 0.15014\n",
      "starting to reduce usage for unit 2971 with usage 1.0219 . Total units tried = 265\n",
      "try to drop unit 2971 from 0 th HD out of 67 possible.  usage down to 1.021895600539649\n",
      "all done trying to reduce usage of unit 2971 final usage = 1.00786 237 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01145 0.15014\n",
      "starting to reduce usage for unit 1591 with usage 1.02172 . Total units tried = 266\n",
      "try to drop unit 1591 from 0 th HD out of 87 possible.  usage down to 1.0217194058566779\n",
      "all done trying to reduce usage of unit 1591 final usage = 1.01227 237 sec elapsed 1 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.01144 0.15012\n",
      "starting to reduce usage for unit 831 with usage 1.0217 . Total units tried = 267\n",
      "try to drop unit 831 from 0 th HD out of 77 possible.  usage down to 1.0217026254106365\n",
      "all done trying to reduce usage of unit 831 final usage = 1.00866 237 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01143 0.15011\n",
      "starting to reduce usage for unit 1758 with usage 1.02165 . Total units tried = 268\n",
      "try to drop unit 1758 from 0 th HD out of 35 possible.  usage down to 1.0216522840727347\n",
      "all done trying to reduce usage of unit 1758 final usage = 1.01389 238 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01143 0.15003\n",
      "starting to reduce usage for unit 119 with usage 1.02155 . Total units tried = 269\n",
      "try to drop unit 119 from 0 th HD out of 81 possible.  usage down to 1.0215516013966963\n",
      "all done trying to reduce usage of unit 119 final usage = 1.01367 238 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01142 0.15002\n",
      "starting to reduce usage for unit 1126 with usage 1.02153 . Total units tried = 270\n",
      "try to drop unit 1126 from 0 th HD out of 106 possible.  usage down to 1.0215348209507822\n",
      "all done trying to reduce usage of unit 1126 final usage = 1.01077 238 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01142 0.15\n",
      "starting to reduce usage for unit 2668 with usage 1.02153 . Total units tried = 271\n",
      "try to drop unit 2668 from 0 th HD out of 86 possible.  usage down to 1.0215264307276812\n",
      "all done trying to reduce usage of unit 2668 final usage = 1.01271 238 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01142 0.15\n",
      "starting to reduce usage for unit 2938 with usage 1.02153 . Total units tried = 272\n",
      "try to drop unit 2938 from 0 th HD out of 95 possible.  usage down to 1.0215264307276608\n",
      "all done trying to reduce usage of unit 2938 final usage = 1.01401 238 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01142 0.14999\n",
      "starting to reduce usage for unit 805 with usage 1.02149 . Total units tried = 273\n",
      "try to drop unit 805 from 0 th HD out of 77 possible.  usage down to 1.0214928698356178\n",
      "all done trying to reduce usage of unit 805 final usage = 1.00595 238 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01141 0.14999\n",
      "starting to reduce usage for unit 980 with usage 1.02343 . Total units tried = 274\n",
      "try to drop unit 980 from 0 th HD out of 78 possible.  usage down to 1.0234310113487335\n",
      "all done trying to reduce usage of unit 980 final usage = 1.01175 239 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01141 0.14999\n",
      "starting to reduce usage for unit 1886 with usage 1.0221 . Total units tried = 275\n",
      "try to drop unit 1886 from 0 th HD out of 77 possible.  usage down to 1.022096965891615\n",
      "all done trying to reduce usage of unit 1886 final usage = 1.01212 239 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0114 0.14998\n",
      "starting to reduce usage for unit 1783 with usage 1.02145 . Total units tried = 276\n",
      "try to drop unit 1783 from 0 th HD out of 79 possible.  usage down to 1.021450918720652\n",
      "all done trying to reduce usage of unit 1783 final usage = 1.00707 239 sec elapsed 1 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01139 0.14998\n",
      "starting to reduce usage for unit 1626 with usage 1.02141 . Total units tried = 277\n",
      "try to drop unit 1626 from 0 th HD out of 85 possible.  usage down to 1.021408967605646\n",
      "all done trying to reduce usage of unit 1626 final usage = 0.99996 239 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0114 0.14997\n",
      "starting to reduce usage for unit 1621 with usage 1.02956 . Total units tried = 278\n",
      "try to drop unit 1621 from 0 th HD out of 68 possible.  usage down to 1.0295642643618954\n",
      "all done trying to reduce usage of unit 1621 final usage = 1.01518 240 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01139 0.14996\n",
      "starting to reduce usage for unit 1770 with usage 1.02298 . Total units tried = 279\n",
      "try to drop unit 1770 from 0 th HD out of 74 possible.  usage down to 1.0229779393066585\n",
      "all done trying to reduce usage of unit 1770 final usage = 1.01244 240 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01138 0.14994\n",
      "starting to reduce usage for unit 394 with usage 1.02138 . Total units tried = 280\n",
      "try to drop unit 394 from 0 th HD out of 82 possible.  usage down to 1.021375406713634\n",
      "all done trying to reduce usage of unit 394 final usage = 1.01585 240 sec elapsed 1 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.01138 0.14994\n",
      "starting to reduce usage for unit 149 with usage 1.02129 . Total units tried = 281\n",
      "try to drop unit 149 from 0 th HD out of 72 possible.  usage down to 1.0212915044836066\n",
      "all done trying to reduce usage of unit 149 final usage = 1.01525 240 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01137 0.14994\n",
      "starting to reduce usage for unit 2399 with usage 1.02122 . Total units tried = 282\n",
      "try to drop unit 2399 from 0 th HD out of 53 possible.  usage down to 1.0212243826998002\n",
      "all done trying to reduce usage of unit 2399 final usage = 1.01368 240 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01137 0.14993\n",
      "starting to reduce usage for unit 2434 with usage 1.02122 . Total units tried = 283\n",
      "try to drop unit 2434 from 0 th HD out of 84 possible.  usage down to 1.0212243826996128\n",
      "all done trying to reduce usage of unit 2434 final usage = 1.01174 240 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01137 0.14993\n",
      "starting to reduce usage for unit 2406 with usage 1.02117 . Total units tried = 284\n",
      "try to drop unit 2406 from 0 th HD out of 104 possible.  usage down to 1.0211656511387244\n",
      "all done trying to reduce usage of unit 2406 final usage = 1.01114 241 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01137 0.14992\n",
      "starting to reduce usage for unit 2918 with usage 1.02114 . Total units tried = 285\n",
      "try to drop unit 2918 from 0 th HD out of 29 possible.  usage down to 1.021140480469697\n",
      "all done trying to reduce usage of unit 2918 final usage = 1.00457 241 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01136 0.14991\n",
      "starting to reduce usage for unit 660 with usage 1.02106 . Total units tried = 286\n",
      "try to drop unit 660 from 0 th HD out of 79 possible.  usage down to 1.0210565782396284\n",
      "all done trying to reduce usage of unit 660 final usage = 1.00701 241 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01136 0.14991\n",
      "starting to reduce usage for unit 1195 with usage 1.023 . Total units tried = 287\n",
      "try to drop unit 1195 from 0 th HD out of 24 possible.  usage down to 1.0230031099757062\n",
      "all done trying to reduce usage of unit 1195 final usage = 1.01482 241 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01136 0.14991\n",
      "starting to reduce usage for unit 2999 with usage 1.02097 . Total units tried = 288\n",
      "try to drop unit 2999 from 0 th HD out of 86 possible.  usage down to 1.0209726760096425\n",
      "all done trying to reduce usage of unit 2999 final usage = 1.00732 241 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01135 0.14991\n",
      "starting to reduce usage for unit 2553 with usage 1.02096 . Total units tried = 289\n",
      "try to drop unit 2553 from 0 th HD out of 92 possible.  usage down to 1.020955895563637\n",
      "all done trying to reduce usage of unit 2553 final usage = 1.011 241 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01135 0.1499\n",
      "starting to reduce usage for unit 2051 with usage 1.02088 . Total units tried = 290\n",
      "try to drop unit 2051 from 0 th HD out of 96 possible.  usage down to 1.020880383556654\n",
      "all done trying to reduce usage of unit 2051 final usage = 1.01257 241 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01134 0.1499\n",
      "starting to reduce usage for unit 52 with usage 1.02084 . Total units tried = 291\n",
      "try to drop unit 52 from 0 th HD out of 55 possible.  usage down to 1.0208384324416575\n",
      "all done trying to reduce usage of unit 52 final usage = 1.00721 242 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01134 0.1499\n",
      "starting to reduce usage for unit 474 with usage 1.02078 . Total units tried = 292\n",
      "try to drop unit 474 from 0 th HD out of 75 possible.  usage down to 1.0207797008806374\n",
      "all done trying to reduce usage of unit 474 final usage = 1.01053 242 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01133 0.1499\n",
      "starting to reduce usage for unit 2356 with usage 1.02077 . Total units tried = 293\n",
      "try to drop unit 2356 from 0 th HD out of 88 possible.  usage down to 1.02077131065767\n",
      "all done trying to reduce usage of unit 2356 final usage = 1.01653 242 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01133 0.1499\n",
      "starting to reduce usage for unit 1870 with usage 1.02072 . Total units tried = 294\n",
      "try to drop unit 1870 from 0 th HD out of 56 possible.  usage down to 1.0207209693195614\n",
      "all done trying to reduce usage of unit 1870 final usage = 1.00996 242 sec elapsed 1 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.01133 0.14989\n",
      "starting to reduce usage for unit 1604 with usage 1.02065 . Total units tried = 295\n",
      "try to drop unit 1604 from 0 th HD out of 84 possible.  usage down to 1.0206538475356137\n",
      "all done trying to reduce usage of unit 1604 final usage = 1.00955 242 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01132 0.14988\n",
      "starting to reduce usage for unit 500 with usage 1.02054 . Total units tried = 296\n",
      "try to drop unit 500 from 0 th HD out of 88 possible.  usage down to 1.0205447746366132\n",
      "all done trying to reduce usage of unit 500 final usage = 1.01018 242 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01132 0.14988\n",
      "starting to reduce usage for unit 2048 with usage 1.02037 . Total units tried = 297\n",
      "try to drop unit 2048 from 0 th HD out of 93 possible.  usage down to 1.0203685799536315\n",
      "all done trying to reduce usage of unit 2048 final usage = 1.00699 242 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01131 0.14988\n",
      "starting to reduce usage for unit 1284 with usage 1.02037 . Total units tried = 298\n",
      "try to drop unit 1284 from 0 th HD out of 68 possible.  usage down to 1.020368579953584\n",
      "all done trying to reduce usage of unit 1284 final usage = 1.01346 243 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01131 0.14987\n",
      "starting to reduce usage for unit 2486 with usage 1.02034 . Total units tried = 299\n",
      "try to drop unit 2486 from 0 th HD out of 80 possible.  usage down to 1.0203350190616207\n",
      "all done trying to reduce usage of unit 2486 final usage = 1.0085 243 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0113 0.14987\n",
      "starting to reduce usage for unit 150 with usage 1.02032 . Total units tried = 300\n",
      "try to drop unit 150 from 0 th HD out of 73 possible.  usage down to 1.020318238615561\n",
      "all done trying to reduce usage of unit 150 final usage = 1.00626 243 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01128 0.14987\n",
      "starting to reduce usage for unit 297 with usage 1.02263 . Total units tried = 301\n",
      "try to drop unit 297 from 0 th HD out of 85 possible.  usage down to 1.0226255499406147\n",
      "all done trying to reduce usage of unit 297 final usage = 1.01203 243 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01128 0.14987\n",
      "starting to reduce usage for unit 829 with usage 1.02028 . Total units tried = 302\n",
      "try to drop unit 829 from 0 th HD out of 97 possible.  usage down to 1.0202846777236219\n",
      "all done trying to reduce usage of unit 829 final usage = 1.00594 244 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01127 0.14985\n",
      "starting to reduce usage for unit 306 with usage 1.02023 . Total units tried = 303\n",
      "try to drop unit 306 from 0 th HD out of 73 possible.  usage down to 1.0202259461625438\n",
      "all done trying to reduce usage of unit 306 final usage = 1.00897 244 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01126 0.14985\n",
      "starting to reduce usage for unit 3169 with usage 1.02016 . Total units tried = 304\n",
      "try to drop unit 3169 from 0 th HD out of 134 possible.  usage down to 1.0201588243787352\n",
      "all done trying to reduce usage of unit 3169 final usage = 1.01773 244 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01126 0.14985\n",
      "starting to reduce usage for unit 836 with usage 1.02016 . Total units tried = 305\n",
      "try to drop unit 836 from 0 th HD out of 89 possible.  usage down to 1.0201588243786643\n",
      "all done trying to reduce usage of unit 836 final usage = 1.01304 244 sec elapsed 1 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1931 with usage 1.02008 . Total units tried = 306\n",
      "try to drop unit 1931 from 0 th HD out of 63 possible.  usage down to 1.0200833123716453\n",
      "all done trying to reduce usage of unit 1931 final usage = 1.01275 244 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 652 with usage 1.01978 . Total units tried = 307\n",
      "all done trying to reduce usage of unit 652 final usage = 1.01978 244 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1353 with usage 1.01977 . Total units tried = 308\n",
      "all done trying to reduce usage of unit 1353 final usage = 1.01977 244 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1127 with usage 1.01976 . Total units tried = 309\n",
      "all done trying to reduce usage of unit 1127 final usage = 1.01976 244 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1817 with usage 1.01976 . Total units tried = 310\n",
      "all done trying to reduce usage of unit 1817 final usage = 1.01976 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 638 with usage 1.01974 . Total units tried = 311\n",
      "all done trying to reduce usage of unit 638 final usage = 1.01974 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2439 with usage 1.01973 . Total units tried = 312\n",
      "all done trying to reduce usage of unit 2439 final usage = 1.01973 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 801 with usage 1.01972 . Total units tried = 313\n",
      "all done trying to reduce usage of unit 801 final usage = 1.01972 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1318 with usage 1.01967 . Total units tried = 314\n",
      "all done trying to reduce usage of unit 1318 final usage = 1.01967 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1733 with usage 1.01964 . Total units tried = 315\n",
      "all done trying to reduce usage of unit 1733 final usage = 1.01964 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 594 with usage 1.01958 . Total units tried = 316\n",
      "all done trying to reduce usage of unit 594 final usage = 1.01958 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3129 with usage 1.01953 . Total units tried = 317\n",
      "all done trying to reduce usage of unit 3129 final usage = 1.01953 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1913 with usage 1.01948 . Total units tried = 318\n",
      "all done trying to reduce usage of unit 1913 final usage = 1.01948 245 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 698 with usage 1.01946 . Total units tried = 319\n",
      "all done trying to reduce usage of unit 698 final usage = 1.01946 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 441 with usage 1.01935 . Total units tried = 320\n",
      "all done trying to reduce usage of unit 441 final usage = 1.01935 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 523 with usage 1.01935 . Total units tried = 321\n",
      "all done trying to reduce usage of unit 523 final usage = 1.01935 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2153 with usage 1.01928 . Total units tried = 322\n",
      "all done trying to reduce usage of unit 2153 final usage = 1.01928 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1805 with usage 1.01928 . Total units tried = 323\n",
      "all done trying to reduce usage of unit 1805 final usage = 1.01928 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 15 with usage 1.01921 . Total units tried = 324\n",
      "all done trying to reduce usage of unit 15 final usage = 1.01921 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 818 with usage 1.01916 . Total units tried = 325\n",
      "all done trying to reduce usage of unit 818 final usage = 1.01916 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 237 with usage 1.01903 . Total units tried = 326\n",
      "all done trying to reduce usage of unit 237 final usage = 1.01903 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3257 with usage 1.01888 . Total units tried = 327\n",
      "all done trying to reduce usage of unit 3257 final usage = 1.01888 246 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1312 with usage 1.01885 . Total units tried = 328\n",
      "all done trying to reduce usage of unit 1312 final usage = 1.01885 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2100 with usage 1.01882 . Total units tried = 329\n",
      "all done trying to reduce usage of unit 2100 final usage = 1.01882 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1829 with usage 1.01878 . Total units tried = 330\n",
      "all done trying to reduce usage of unit 1829 final usage = 1.01878 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 31 with usage 1.01877 . Total units tried = 331\n",
      "all done trying to reduce usage of unit 31 final usage = 1.01877 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2975 with usage 1.01873 . Total units tried = 332\n",
      "all done trying to reduce usage of unit 2975 final usage = 1.01873 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 409 with usage 1.0187 . Total units tried = 333\n",
      "all done trying to reduce usage of unit 409 final usage = 1.0187 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2966 with usage 1.01869 . Total units tried = 334\n",
      "all done trying to reduce usage of unit 2966 final usage = 1.01869 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2991 with usage 1.01866 . Total units tried = 335\n",
      "all done trying to reduce usage of unit 2991 final usage = 1.01866 247 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 518 with usage 1.01861 . Total units tried = 336\n",
      "all done trying to reduce usage of unit 518 final usage = 1.01861 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 917 with usage 1.01855 . Total units tried = 337\n",
      "all done trying to reduce usage of unit 917 final usage = 1.01855 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 726 with usage 1.01855 . Total units tried = 338\n",
      "all done trying to reduce usage of unit 726 final usage = 1.01855 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1766 with usage 1.01851 . Total units tried = 339\n",
      "all done trying to reduce usage of unit 1766 final usage = 1.01851 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 574 with usage 1.01851 . Total units tried = 340\n",
      "all done trying to reduce usage of unit 574 final usage = 1.01851 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2247 with usage 1.01846 . Total units tried = 341\n",
      "all done trying to reduce usage of unit 2247 final usage = 1.01846 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 530 with usage 1.01841 . Total units tried = 342\n",
      "all done trying to reduce usage of unit 530 final usage = 1.01841 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2921 with usage 1.0183 . Total units tried = 343\n",
      "all done trying to reduce usage of unit 2921 final usage = 1.0183 248 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 596 with usage 1.0183 . Total units tried = 344\n",
      "all done trying to reduce usage of unit 596 final usage = 1.0183 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2494 with usage 1.01827 . Total units tried = 345\n",
      "all done trying to reduce usage of unit 2494 final usage = 1.01827 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3023 with usage 1.01823 . Total units tried = 346\n",
      "all done trying to reduce usage of unit 3023 final usage = 1.01823 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 480 with usage 1.01822 . Total units tried = 347\n",
      "all done trying to reduce usage of unit 480 final usage = 1.01822 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 157 with usage 1.01819 . Total units tried = 348\n",
      "all done trying to reduce usage of unit 157 final usage = 1.01819 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 847 with usage 1.01817 . Total units tried = 349\n",
      "all done trying to reduce usage of unit 847 final usage = 1.01817 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 773 with usage 1.01815 . Total units tried = 350\n",
      "all done trying to reduce usage of unit 773 final usage = 1.01815 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 160 with usage 1.01814 . Total units tried = 351\n",
      "all done trying to reduce usage of unit 160 final usage = 1.01814 249 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3063 with usage 1.0181 . Total units tried = 352\n",
      "all done trying to reduce usage of unit 3063 final usage = 1.0181 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2099 with usage 1.0181 . Total units tried = 353\n",
      "all done trying to reduce usage of unit 2099 final usage = 1.0181 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 724 with usage 1.01809 . Total units tried = 354\n",
      "all done trying to reduce usage of unit 724 final usage = 1.01809 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2033 with usage 1.01808 . Total units tried = 355\n",
      "all done trying to reduce usage of unit 2033 final usage = 1.01808 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2548 with usage 1.01807 . Total units tried = 356\n",
      "all done trying to reduce usage of unit 2548 final usage = 1.01807 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 501 with usage 1.01803 . Total units tried = 357\n",
      "all done trying to reduce usage of unit 501 final usage = 1.01803 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1818 with usage 1.01803 . Total units tried = 358\n",
      "all done trying to reduce usage of unit 1818 final usage = 1.01803 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2893 with usage 1.01798 . Total units tried = 359\n",
      "all done trying to reduce usage of unit 2893 final usage = 1.01798 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2273 with usage 1.01791 . Total units tried = 360\n",
      "all done trying to reduce usage of unit 2273 final usage = 1.01791 250 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2182 with usage 1.01789 . Total units tried = 361\n",
      "all done trying to reduce usage of unit 2182 final usage = 1.01789 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2191 with usage 1.01783 . Total units tried = 362\n",
      "all done trying to reduce usage of unit 2191 final usage = 1.01783 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2414 with usage 1.01775 . Total units tried = 363\n",
      "all done trying to reduce usage of unit 2414 final usage = 1.01775 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1300 with usage 1.01766 . Total units tried = 364\n",
      "all done trying to reduce usage of unit 1300 final usage = 1.01766 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2990 with usage 1.01766 . Total units tried = 365\n",
      "all done trying to reduce usage of unit 2990 final usage = 1.01766 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 350 with usage 1.01758 . Total units tried = 366\n",
      "all done trying to reduce usage of unit 350 final usage = 1.01758 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 861 with usage 1.01754 . Total units tried = 367\n",
      "all done trying to reduce usage of unit 861 final usage = 1.01754 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 899 with usage 1.01752 . Total units tried = 368\n",
      "all done trying to reduce usage of unit 899 final usage = 1.01752 251 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1125 with usage 1.01748 . Total units tried = 369\n",
      "all done trying to reduce usage of unit 1125 final usage = 1.01748 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1072 with usage 1.01744 . Total units tried = 370\n",
      "all done trying to reduce usage of unit 1072 final usage = 1.01744 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2452 with usage 1.01742 . Total units tried = 371\n",
      "all done trying to reduce usage of unit 2452 final usage = 1.01742 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 301 with usage 1.01742 . Total units tried = 372\n",
      "all done trying to reduce usage of unit 301 final usage = 1.01742 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1988 with usage 1.01731 . Total units tried = 373\n",
      "all done trying to reduce usage of unit 1988 final usage = 1.01731 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 775 with usage 1.01727 . Total units tried = 374\n",
      "all done trying to reduce usage of unit 775 final usage = 1.01727 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2149 with usage 1.01726 . Total units tried = 375\n",
      "all done trying to reduce usage of unit 2149 final usage = 1.01726 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 256 with usage 1.01725 . Total units tried = 376\n",
      "all done trying to reduce usage of unit 256 final usage = 1.01725 252 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1006 with usage 1.01722 . Total units tried = 377\n",
      "all done trying to reduce usage of unit 1006 final usage = 1.01722 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 404 with usage 1.01715 . Total units tried = 378\n",
      "all done trying to reduce usage of unit 404 final usage = 1.01715 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2487 with usage 1.01715 . Total units tried = 379\n",
      "all done trying to reduce usage of unit 2487 final usage = 1.01715 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2621 with usage 1.01713 . Total units tried = 380\n",
      "all done trying to reduce usage of unit 2621 final usage = 1.01713 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2050 with usage 1.01705 . Total units tried = 381\n",
      "all done trying to reduce usage of unit 2050 final usage = 1.01705 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1769 with usage 1.017 . Total units tried = 382\n",
      "all done trying to reduce usage of unit 1769 final usage = 1.017 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 21 with usage 1.01698 . Total units tried = 383\n",
      "all done trying to reduce usage of unit 21 final usage = 1.01698 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1274 with usage 1.01696 . Total units tried = 384\n",
      "all done trying to reduce usage of unit 1274 final usage = 1.01696 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1159 with usage 1.01677 . Total units tried = 385\n",
      "all done trying to reduce usage of unit 1159 final usage = 1.01677 253 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2509 with usage 1.01676 . Total units tried = 386\n",
      "all done trying to reduce usage of unit 2509 final usage = 1.01676 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 623 with usage 1.01675 . Total units tried = 387\n",
      "all done trying to reduce usage of unit 623 final usage = 1.01675 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1110 with usage 1.01674 . Total units tried = 388\n",
      "all done trying to reduce usage of unit 1110 final usage = 1.01674 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 765 with usage 1.01671 . Total units tried = 389\n",
      "all done trying to reduce usage of unit 765 final usage = 1.01671 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1316 with usage 1.01668 . Total units tried = 390\n",
      "all done trying to reduce usage of unit 1316 final usage = 1.01668 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2612 with usage 1.01665 . Total units tried = 391\n",
      "all done trying to reduce usage of unit 2612 final usage = 1.01665 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2172 with usage 1.01663 . Total units tried = 392\n",
      "all done trying to reduce usage of unit 2172 final usage = 1.01663 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2071 with usage 1.01657 . Total units tried = 393\n",
      "all done trying to reduce usage of unit 2071 final usage = 1.01657 254 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 544 with usage 1.01656 . Total units tried = 394\n",
      "all done trying to reduce usage of unit 544 final usage = 1.01656 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 554 with usage 1.01656 . Total units tried = 395\n",
      "all done trying to reduce usage of unit 554 final usage = 1.01656 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2490 with usage 1.01654 . Total units tried = 396\n",
      "all done trying to reduce usage of unit 2490 final usage = 1.01654 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 925 with usage 1.01649 . Total units tried = 397\n",
      "all done trying to reduce usage of unit 925 final usage = 1.01649 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 156 with usage 1.01648 . Total units tried = 398\n",
      "all done trying to reduce usage of unit 156 final usage = 1.01648 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 455 with usage 1.01647 . Total units tried = 399\n",
      "all done trying to reduce usage of unit 455 final usage = 1.01647 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2086 with usage 1.01645 . Total units tried = 400\n",
      "all done trying to reduce usage of unit 2086 final usage = 1.01645 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1021 with usage 1.01642 . Total units tried = 401\n",
      "all done trying to reduce usage of unit 1021 final usage = 1.01642 255 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 651 with usage 1.01641 . Total units tried = 402\n",
      "all done trying to reduce usage of unit 651 final usage = 1.01641 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3201 with usage 1.0164 . Total units tried = 403\n",
      "all done trying to reduce usage of unit 3201 final usage = 1.0164 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1327 with usage 1.01638 . Total units tried = 404\n",
      "all done trying to reduce usage of unit 1327 final usage = 1.01638 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2537 with usage 1.01632 . Total units tried = 405\n",
      "all done trying to reduce usage of unit 2537 final usage = 1.01632 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1194 with usage 1.01627 . Total units tried = 406\n",
      "all done trying to reduce usage of unit 1194 final usage = 1.01627 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 683 with usage 1.01625 . Total units tried = 407\n",
      "all done trying to reduce usage of unit 683 final usage = 1.01625 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 699 with usage 1.01621 . Total units tried = 408\n",
      "all done trying to reduce usage of unit 699 final usage = 1.01621 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 42 with usage 1.01617 . Total units tried = 409\n",
      "all done trying to reduce usage of unit 42 final usage = 1.01617 256 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2415 with usage 1.01617 . Total units tried = 410\n",
      "all done trying to reduce usage of unit 2415 final usage = 1.01617 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1068 with usage 1.01614 . Total units tried = 411\n",
      "all done trying to reduce usage of unit 1068 final usage = 1.01614 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3258 with usage 1.01611 . Total units tried = 412\n",
      "all done trying to reduce usage of unit 3258 final usage = 1.01611 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 141 with usage 1.01601 . Total units tried = 413\n",
      "all done trying to reduce usage of unit 141 final usage = 1.01601 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 573 with usage 1.016 . Total units tried = 414\n",
      "all done trying to reduce usage of unit 573 final usage = 1.016 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 702 with usage 1.016 . Total units tried = 415\n",
      "all done trying to reduce usage of unit 702 final usage = 1.016 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3105 with usage 1.01598 . Total units tried = 416\n",
      "all done trying to reduce usage of unit 3105 final usage = 1.01598 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2206 with usage 1.01596 . Total units tried = 417\n",
      "all done trying to reduce usage of unit 2206 final usage = 1.01596 257 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2425 with usage 1.01596 . Total units tried = 418\n",
      "all done trying to reduce usage of unit 2425 final usage = 1.01596 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2944 with usage 1.01595 . Total units tried = 419\n",
      "all done trying to reduce usage of unit 2944 final usage = 1.01595 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2028 with usage 1.01594 . Total units tried = 420\n",
      "all done trying to reduce usage of unit 2028 final usage = 1.01594 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 654 with usage 1.01592 . Total units tried = 421\n",
      "all done trying to reduce usage of unit 654 final usage = 1.01592 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 267 with usage 1.01592 . Total units tried = 422\n",
      "all done trying to reduce usage of unit 267 final usage = 1.01592 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1291 with usage 1.01591 . Total units tried = 423\n",
      "all done trying to reduce usage of unit 1291 final usage = 1.01591 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 443 with usage 1.01588 . Total units tried = 424\n",
      "all done trying to reduce usage of unit 443 final usage = 1.01588 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1333 with usage 1.01587 . Total units tried = 425\n",
      "all done trying to reduce usage of unit 1333 final usage = 1.01587 258 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 336 with usage 1.01585 . Total units tried = 426\n",
      "all done trying to reduce usage of unit 336 final usage = 1.01585 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 811 with usage 1.01575 . Total units tried = 427\n",
      "all done trying to reduce usage of unit 811 final usage = 1.01575 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2413 with usage 1.01575 . Total units tried = 428\n",
      "all done trying to reduce usage of unit 2413 final usage = 1.01575 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 436 with usage 1.0157 . Total units tried = 429\n",
      "all done trying to reduce usage of unit 436 final usage = 1.0157 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2029 with usage 1.01567 . Total units tried = 430\n",
      "all done trying to reduce usage of unit 2029 final usage = 1.01567 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2491 with usage 1.01566 . Total units tried = 431\n",
      "all done trying to reduce usage of unit 2491 final usage = 1.01566 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2152 with usage 1.01566 . Total units tried = 432\n",
      "all done trying to reduce usage of unit 2152 final usage = 1.01566 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 977 with usage 1.01566 . Total units tried = 433\n",
      "all done trying to reduce usage of unit 977 final usage = 1.01566 259 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2528 with usage 1.01561 . Total units tried = 434\n",
      "all done trying to reduce usage of unit 2528 final usage = 1.01561 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1835 with usage 1.01559 . Total units tried = 435\n",
      "all done trying to reduce usage of unit 1835 final usage = 1.01559 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1789 with usage 1.01556 . Total units tried = 436\n",
      "all done trying to reduce usage of unit 1789 final usage = 1.01556 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 959 with usage 1.01556 . Total units tried = 437\n",
      "all done trying to reduce usage of unit 959 final usage = 1.01556 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2599 with usage 1.01554 . Total units tried = 438\n",
      "all done trying to reduce usage of unit 2599 final usage = 1.01554 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2905 with usage 1.01554 . Total units tried = 439\n",
      "all done trying to reduce usage of unit 2905 final usage = 1.01554 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2284 with usage 1.01546 . Total units tried = 440\n",
      "all done trying to reduce usage of unit 2284 final usage = 1.01546 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 389 with usage 1.01545 . Total units tried = 441\n",
      "all done trying to reduce usage of unit 389 final usage = 1.01545 260 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2296 with usage 1.01544 . Total units tried = 442\n",
      "all done trying to reduce usage of unit 2296 final usage = 1.01544 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1642 with usage 1.01544 . Total units tried = 443\n",
      "all done trying to reduce usage of unit 1642 final usage = 1.01544 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2483 with usage 1.01538 . Total units tried = 444\n",
      "all done trying to reduce usage of unit 2483 final usage = 1.01538 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 130 with usage 1.01538 . Total units tried = 445\n",
      "all done trying to reduce usage of unit 130 final usage = 1.01538 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2475 with usage 1.01536 . Total units tried = 446\n",
      "all done trying to reduce usage of unit 2475 final usage = 1.01536 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1609 with usage 1.01535 . Total units tried = 447\n",
      "all done trying to reduce usage of unit 1609 final usage = 1.01535 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3095 with usage 1.0153 . Total units tried = 448\n",
      "all done trying to reduce usage of unit 3095 final usage = 1.0153 261 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1836 with usage 1.01526 . Total units tried = 449\n",
      "all done trying to reduce usage of unit 1836 final usage = 1.01526 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 735 with usage 1.01518 . Total units tried = 450\n",
      "all done trying to reduce usage of unit 735 final usage = 1.01518 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2216 with usage 1.01514 . Total units tried = 451\n",
      "all done trying to reduce usage of unit 2216 final usage = 1.01514 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 365 with usage 1.01502 . Total units tried = 452\n",
      "all done trying to reduce usage of unit 365 final usage = 1.01502 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 359 with usage 1.01502 . Total units tried = 453\n",
      "all done trying to reduce usage of unit 359 final usage = 1.01502 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1004 with usage 1.01501 . Total units tried = 454\n",
      "all done trying to reduce usage of unit 1004 final usage = 1.01501 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2577 with usage 1.015 . Total units tried = 455\n",
      "all done trying to reduce usage of unit 2577 final usage = 1.015 262 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2444 with usage 1.01499 . Total units tried = 456\n",
      "all done trying to reduce usage of unit 2444 final usage = 1.01499 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1161 with usage 1.01498 . Total units tried = 457\n",
      "all done trying to reduce usage of unit 1161 final usage = 1.01498 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2519 with usage 1.01488 . Total units tried = 458\n",
      "all done trying to reduce usage of unit 2519 final usage = 1.01488 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1122 with usage 1.01488 . Total units tried = 459\n",
      "all done trying to reduce usage of unit 1122 final usage = 1.01488 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1363 with usage 1.01486 . Total units tried = 460\n",
      "all done trying to reduce usage of unit 1363 final usage = 1.01486 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1201 with usage 1.01486 . Total units tried = 461\n",
      "all done trying to reduce usage of unit 1201 final usage = 1.01486 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1086 with usage 1.01486 . Total units tried = 462\n",
      "all done trying to reduce usage of unit 1086 final usage = 1.01486 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 91 with usage 1.01486 . Total units tried = 463\n",
      "all done trying to reduce usage of unit 91 final usage = 1.01486 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 996 with usage 1.01481 . Total units tried = 464\n",
      "all done trying to reduce usage of unit 996 final usage = 1.01481 263 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 736 with usage 1.01481 . Total units tried = 465\n",
      "all done trying to reduce usage of unit 736 final usage = 1.01481 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2328 with usage 1.01476 . Total units tried = 466\n",
      "all done trying to reduce usage of unit 2328 final usage = 1.01476 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 502 with usage 1.01475 . Total units tried = 467\n",
      "all done trying to reduce usage of unit 502 final usage = 1.01475 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2576 with usage 1.01469 . Total units tried = 468\n",
      "all done trying to reduce usage of unit 2576 final usage = 1.01469 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2175 with usage 1.01467 . Total units tried = 469\n",
      "all done trying to reduce usage of unit 2175 final usage = 1.01467 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2208 with usage 1.01466 . Total units tried = 470\n",
      "all done trying to reduce usage of unit 2208 final usage = 1.01466 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3046 with usage 1.01465 . Total units tried = 471\n",
      "all done trying to reduce usage of unit 3046 final usage = 1.01465 264 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3407 with usage 1.01456 . Total units tried = 472\n",
      "all done trying to reduce usage of unit 3407 final usage = 1.01456 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1812 with usage 1.01454 . Total units tried = 473\n",
      "all done trying to reduce usage of unit 1812 final usage = 1.01454 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2954 with usage 1.01452 . Total units tried = 474\n",
      "all done trying to reduce usage of unit 2954 final usage = 1.01452 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 379 with usage 1.0145 . Total units tried = 475\n",
      "all done trying to reduce usage of unit 379 final usage = 1.0145 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2446 with usage 1.01445 . Total units tried = 476\n",
      "all done trying to reduce usage of unit 2446 final usage = 1.01445 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 413 with usage 1.01443 . Total units tried = 477\n",
      "all done trying to reduce usage of unit 413 final usage = 1.01443 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 245 with usage 1.01439 . Total units tried = 478\n",
      "all done trying to reduce usage of unit 245 final usage = 1.01439 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1233 with usage 1.01439 . Total units tried = 479\n",
      "all done trying to reduce usage of unit 1233 final usage = 1.01439 265 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 895 with usage 1.01439 . Total units tried = 480\n",
      "all done trying to reduce usage of unit 895 final usage = 1.01439 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1154 with usage 1.01438 . Total units tried = 481\n",
      "all done trying to reduce usage of unit 1154 final usage = 1.01438 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2129 with usage 1.01433 . Total units tried = 482\n",
      "all done trying to reduce usage of unit 2129 final usage = 1.01433 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3139 with usage 1.01428 . Total units tried = 483\n",
      "all done trying to reduce usage of unit 3139 final usage = 1.01428 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 422 with usage 1.01427 . Total units tried = 484\n",
      "all done trying to reduce usage of unit 422 final usage = 1.01427 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1663 with usage 1.01424 . Total units tried = 485\n",
      "all done trying to reduce usage of unit 1663 final usage = 1.01424 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 291 with usage 1.01419 . Total units tried = 486\n",
      "all done trying to reduce usage of unit 291 final usage = 1.01419 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 688 with usage 1.01416 . Total units tried = 487\n",
      "all done trying to reduce usage of unit 688 final usage = 1.01416 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3034 with usage 1.01408 . Total units tried = 488\n",
      "all done trying to reduce usage of unit 3034 final usage = 1.01408 266 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 655 with usage 1.01407 . Total units tried = 489\n",
      "all done trying to reduce usage of unit 655 final usage = 1.01407 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 288 with usage 1.01403 . Total units tried = 490\n",
      "all done trying to reduce usage of unit 288 final usage = 1.01403 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2313 with usage 1.01397 . Total units tried = 491\n",
      "all done trying to reduce usage of unit 2313 final usage = 1.01397 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1366 with usage 1.01395 . Total units tried = 492\n",
      "all done trying to reduce usage of unit 1366 final usage = 1.01395 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 522 with usage 1.01392 . Total units tried = 493\n",
      "all done trying to reduce usage of unit 522 final usage = 1.01392 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1191 with usage 1.01388 . Total units tried = 494\n",
      "all done trying to reduce usage of unit 1191 final usage = 1.01388 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 619 with usage 1.01387 . Total units tried = 495\n",
      "all done trying to reduce usage of unit 619 final usage = 1.01387 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3208 with usage 1.01387 . Total units tried = 496\n",
      "all done trying to reduce usage of unit 3208 final usage = 1.01387 267 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 491 with usage 1.01386 . Total units tried = 497\n",
      "all done trying to reduce usage of unit 491 final usage = 1.01386 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1124 with usage 1.01382 . Total units tried = 498\n",
      "all done trying to reduce usage of unit 1124 final usage = 1.01382 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1370 with usage 1.01382 . Total units tried = 499\n",
      "all done trying to reduce usage of unit 1370 final usage = 1.01382 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 701 with usage 1.01382 . Total units tried = 500\n",
      "all done trying to reduce usage of unit 701 final usage = 1.01382 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1579 with usage 1.01376 . Total units tried = 501\n",
      "all done trying to reduce usage of unit 1579 final usage = 1.01376 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 270 with usage 1.01374 . Total units tried = 502\n",
      "all done trying to reduce usage of unit 270 final usage = 1.01374 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1777 with usage 1.01373 . Total units tried = 503\n",
      "all done trying to reduce usage of unit 1777 final usage = 1.01373 268 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 957 with usage 1.01373 . Total units tried = 504\n",
      "all done trying to reduce usage of unit 957 final usage = 1.01373 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1756 with usage 1.01372 . Total units tried = 505\n",
      "all done trying to reduce usage of unit 1756 final usage = 1.01372 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1809 with usage 1.01372 . Total units tried = 506\n",
      "all done trying to reduce usage of unit 1809 final usage = 1.01372 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2263 with usage 1.01363 . Total units tried = 507\n",
      "all done trying to reduce usage of unit 2263 final usage = 1.01363 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2538 with usage 1.01363 . Total units tried = 508\n",
      "all done trying to reduce usage of unit 2538 final usage = 1.01363 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1225 with usage 1.01363 . Total units tried = 509\n",
      "all done trying to reduce usage of unit 1225 final usage = 1.01363 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2624 with usage 1.01359 . Total units tried = 510\n",
      "all done trying to reduce usage of unit 2624 final usage = 1.01359 269 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3214 with usage 1.01356 . Total units tried = 511\n",
      "all done trying to reduce usage of unit 3214 final usage = 1.01356 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2503 with usage 1.01353 . Total units tried = 512\n",
      "all done trying to reduce usage of unit 2503 final usage = 1.01353 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1330 with usage 1.01352 . Total units tried = 513\n",
      "all done trying to reduce usage of unit 1330 final usage = 1.01352 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 636 with usage 1.01352 . Total units tried = 514\n",
      "all done trying to reduce usage of unit 636 final usage = 1.01352 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3012 with usage 1.01351 . Total units tried = 515\n",
      "all done trying to reduce usage of unit 3012 final usage = 1.01351 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1427 with usage 1.01346 . Total units tried = 516\n",
      "all done trying to reduce usage of unit 1427 final usage = 1.01346 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1794 with usage 1.01343 . Total units tried = 517\n",
      "all done trying to reduce usage of unit 1794 final usage = 1.01343 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 230 with usage 1.01337 . Total units tried = 518\n",
      "all done trying to reduce usage of unit 230 final usage = 1.01337 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2563 with usage 1.01337 . Total units tried = 519\n",
      "all done trying to reduce usage of unit 2563 final usage = 1.01337 270 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2275 with usage 1.01335 . Total units tried = 520\n",
      "all done trying to reduce usage of unit 2275 final usage = 1.01335 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2340 with usage 1.01335 . Total units tried = 521\n",
      "all done trying to reduce usage of unit 2340 final usage = 1.01335 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2178 with usage 1.01332 . Total units tried = 522\n",
      "all done trying to reduce usage of unit 2178 final usage = 1.01332 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 287 with usage 1.01331 . Total units tried = 523\n",
      "all done trying to reduce usage of unit 287 final usage = 1.01331 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1256 with usage 1.01328 . Total units tried = 524\n",
      "all done trying to reduce usage of unit 1256 final usage = 1.01328 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 676 with usage 1.01324 . Total units tried = 525\n",
      "all done trying to reduce usage of unit 676 final usage = 1.01324 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 257 with usage 1.01321 . Total units tried = 526\n",
      "all done trying to reduce usage of unit 257 final usage = 1.01321 271 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2775 with usage 1.01319 . Total units tried = 527\n",
      "all done trying to reduce usage of unit 2775 final usage = 1.01319 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 174 with usage 1.01317 . Total units tried = 528\n",
      "all done trying to reduce usage of unit 174 final usage = 1.01317 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 519 with usage 1.01316 . Total units tried = 529\n",
      "all done trying to reduce usage of unit 519 final usage = 1.01316 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 165 with usage 1.01316 . Total units tried = 530\n",
      "all done trying to reduce usage of unit 165 final usage = 1.01316 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 585 with usage 1.01315 . Total units tried = 531\n",
      "all done trying to reduce usage of unit 585 final usage = 1.01315 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 269 with usage 1.01315 . Total units tried = 532\n",
      "all done trying to reduce usage of unit 269 final usage = 1.01315 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1186 with usage 1.01313 . Total units tried = 533\n",
      "all done trying to reduce usage of unit 1186 final usage = 1.01313 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 397 with usage 1.01307 . Total units tried = 534\n",
      "all done trying to reduce usage of unit 397 final usage = 1.01307 272 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3031 with usage 1.01306 . Total units tried = 535\n",
      "all done trying to reduce usage of unit 3031 final usage = 1.01306 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 310 with usage 1.01304 . Total units tried = 536\n",
      "all done trying to reduce usage of unit 310 final usage = 1.01304 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2992 with usage 1.01301 . Total units tried = 537\n",
      "all done trying to reduce usage of unit 2992 final usage = 1.01301 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 616 with usage 1.01297 . Total units tried = 538\n",
      "all done trying to reduce usage of unit 616 final usage = 1.01297 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2578 with usage 1.01297 . Total units tried = 539\n",
      "all done trying to reduce usage of unit 2578 final usage = 1.01297 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 162 with usage 1.01295 . Total units tried = 540\n",
      "all done trying to reduce usage of unit 162 final usage = 1.01295 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1593 with usage 1.01294 . Total units tried = 541\n",
      "all done trying to reduce usage of unit 1593 final usage = 1.01294 273 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1028 with usage 1.01293 . Total units tried = 542\n",
      "all done trying to reduce usage of unit 1028 final usage = 1.01293 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3035 with usage 1.01292 . Total units tried = 543\n",
      "all done trying to reduce usage of unit 3035 final usage = 1.01292 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 3006 with usage 1.01289 . Total units tried = 544\n",
      "all done trying to reduce usage of unit 3006 final usage = 1.01289 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 1163 with usage 1.01289 . Total units tried = 545\n",
      "all done trying to reduce usage of unit 1163 final usage = 1.01289 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 2866 with usage 1.01289 . Total units tried = 546\n",
      "all done trying to reduce usage of unit 2866 final usage = 1.01289 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 369 with usage 1.01283 . Total units tried = 547\n",
      "all done trying to reduce usage of unit 369 final usage = 1.01283 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 923 with usage 1.01282 . Total units tried = 548\n",
      "all done trying to reduce usage of unit 923 final usage = 1.01282 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n",
      "starting to reduce usage for unit 375 with usage 1.01281 . Total units tried = 549\n",
      "all done trying to reduce usage of unit 375 final usage = 1.01281 274 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01125 0.14983\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "maxSD = 0.08 #0.06  #0.08  #0.04 \n",
    "print(maxSD,\"= default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\")\n",
    "maxSD = float(input(\"enter maxSD\"))\n",
    "stopMaxUse = 1.02 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.15*aDP  #0.15 or 0.25 is debatable\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #\n",
    "maxGap = aDP - minDistrictPop  #0.9 * np.median(unitPop) # = aDP - minDistrictPop\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()\n",
    "for t in popHDlist:\n",
    "    if t not in hasSplitUnit: #hasSplitUnit[t] != 1:  #don't mess with the small number of HDs that have to have a split muni\n",
    "        nonfrozenList.append(t)\n",
    "        \n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #simplified vs. vanillaHD - just take the biggest overuser\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedBigUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = -999.  #preventing re-selection\n",
    "    OUU = eligUnitUse.index(np.max(eligUnitUse))\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    if unitPop[OUU] > 0.25 * aDP:  #some overusers are high-pop.  If so, will drop only this unit, not any neighbors\n",
    "        OUUc = [OUU]\n",
    "    else:\n",
    "        OUUc = [OUU] + unitNbrs[OUU]\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUUcSet = set( OUUc)  #for muni-snap, overused are isolated.  So don't bother adding two-level neighbors to cluster\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t] and homeU[t] != OUU: #can't drop the home muni\n",
    "            HDadjoinSet =   set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            OUUcAdjoinSet = set( getAdjoiners(list(OUUcSet),unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUUcAdjoinSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.8*len(ouuHDs) : #arbitrarily only examine 80% of HDs, biasing farthest ones       \n",
    "        if idxNo % 30 == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD out of\",len(ouuHDs),\"possible.  usage down to\",unitUse[OUU] )\n",
    "        idxNo -=1\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        thisLoopOUUc = [OUU]\n",
    "        possibleOUUc = list((  OUUcSet.intersection(set(HDunitList[t]) )  ).difference( {homeU[t]} )) #again, homeU is untouchable\n",
    "        if np.sum([unitPop[u] for u in possibleOUUc]) < 0.25*aDP:\n",
    "            thisLoopOUUc = possibleOUUc.copy() #we can add the whole non-HDincluded cluster (-homeU) without exceeding 0.25 aDP            \n",
    "        trySet = set(HDunitList[t]).difference(set(thisLoopOUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(thisLoopOUUc))  #set(OUUc)\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates and u != homeU[t]:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative) \n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "35140f52-2cb9-40e6-be15-ac02ba88316c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 1.01125 and SD 0.14983\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        patchedUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a1885f4f-4d84-40b7-b564-e9c0c7d9df12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unitUse by unitPop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overPatchedUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "plt.scatter(unitPop,patchedUse,label=\"patched\")\n",
    "#plt.scatter(unitPop,unpatchedUnitUse,label=\"unpatched\")\n",
    "print(\"unitUse by unitPop\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f81ae057-f060-4c95-9857-bcf3cfa8e82b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "another intermediate save - this time after patching, but still in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"another intermediate save - this time after patching, but still in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"COI1850overP0150.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "65a206ba-dee9-40df-a478-3649c1670f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [overPatchedUnitList[t].copy() for t in range(nHDs)]  #restart\n",
    "unitUse, HDvPop = [0.]*nUnits,  [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "751ccd7b-54a6-4757-b5fc-444db70f8fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are still biased toward high overuse for large units, so do another round of overpatch\n",
      "0.08 = default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxSD 0.08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 40 units out of 3438 with usage above 1.02\n",
      "maxExchangePop is currently 0.15 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 17877\n",
      "current avg and SD of unit usage are 1.01125 0.14983 . Now trying to reduce up to 40 units' overusage\n",
      "starting to reduce usage for unit 1387 with usage 1.80303 . Total units tried = 1\n",
      "try to drop unit 1387 from 0 th HD out of 119 possible.  usage down to 1.8030253618637764\n",
      "try to drop unit 1387 from 30 th HD out of 119 possible.  usage down to 1.7505612974431495\n",
      "try to drop unit 1387 from 60 th HD out of 119 possible.  usage down to 1.7505612974431495\n",
      "try to drop unit 1387 from 90 th HD out of 119 possible.  usage down to 1.73947781285981\n",
      "all done trying to reduce usage of unit 1387 final usage = 1.73948 0 sec elapsed 6 5 successful, failed patches, couldn't start= 85\n",
      "current avg and SD of usage are 1.01122 0.14856\n",
      "starting to reduce usage for unit 3436 with usage 1.65983 . Total units tried = 2\n",
      "try to drop unit 3436 from 0 th HD out of 91 possible.  usage down to 1.6598294259185888\n",
      "try to drop unit 3436 from 30 th HD out of 91 possible.  usage down to 1.637721188312906\n",
      "try to drop unit 3436 from 60 th HD out of 91 possible.  usage down to 1.637721188312906\n",
      "all done trying to reduce usage of unit 3436 final usage = 1.63772 1 sec elapsed 2 0 successful, failed patches, couldn't start= 71\n",
      "current avg and SD of usage are 1.0112 0.14783\n",
      "starting to reduce usage for unit 449 with usage 1.62939 . Total units tried = 3\n",
      "try to drop unit 449 from 0 th HD out of 81 possible.  usage down to 1.6293896968735002\n",
      "try to drop unit 449 from 30 th HD out of 81 possible.  usage down to 1.6058886822497653\n",
      "try to drop unit 449 from 60 th HD out of 81 possible.  usage down to 1.5876902885622055\n",
      "all done trying to reduce usage of unit 449 final usage = 1.58769 2 sec elapsed 4 8 successful, failed patches, couldn't start= 53\n",
      "current avg and SD of usage are 1.0112 0.14647\n",
      "starting to reduce usage for unit 830 with usage 1.60743 . Total units tried = 4\n",
      "try to drop unit 830 from 0 th HD out of 85 possible.  usage down to 1.6074324832817242\n",
      "try to drop unit 830 from 30 th HD out of 85 possible.  usage down to 1.6074324832817242\n",
      "try to drop unit 830 from 60 th HD out of 85 possible.  usage down to 1.6074324832817242\n",
      "all done trying to reduce usage of unit 830 final usage = 1.60743 3 sec elapsed 0 0 successful, failed patches, couldn't start= 68\n",
      "current avg and SD of usage are 1.0112 0.14647\n",
      "starting to reduce usage for unit 592 with usage 1.48322 . Total units tried = 5\n",
      "try to drop unit 592 from 0 th HD out of 86 possible.  usage down to 1.4832236219859851\n",
      "try to drop unit 592 from 30 th HD out of 86 possible.  usage down to 1.4832236219859851\n",
      "try to drop unit 592 from 60 th HD out of 86 possible.  usage down to 1.4832236219859851\n",
      "all done trying to reduce usage of unit 592 final usage = 1.48322 3 sec elapsed 0 0 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.0112 0.14647\n",
      "starting to reduce usage for unit 587 with usage 1.44637 . Total units tried = 6\n",
      "try to drop unit 587 from 0 th HD out of 90 possible.  usage down to 1.4463653723458774\n",
      "try to drop unit 587 from 30 th HD out of 90 possible.  usage down to 1.4241061107261912\n",
      "try to drop unit 587 from 60 th HD out of 90 possible.  usage down to 1.4241061107261912\n",
      "all done trying to reduce usage of unit 587 final usage = 1.42411 5 sec elapsed 3 0 successful, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.01119 0.14598\n",
      "starting to reduce usage for unit 2557 with usage 1.38425 . Total units tried = 7\n",
      "try to drop unit 2557 from 0 th HD out of 92 possible.  usage down to 1.3842525514750113\n",
      "try to drop unit 2557 from 30 th HD out of 92 possible.  usage down to 1.3842525514750113\n",
      "try to drop unit 2557 from 60 th HD out of 92 possible.  usage down to 1.3842525514750113\n",
      "all done trying to reduce usage of unit 2557 final usage = 1.38425 5 sec elapsed 0 0 successful, failed patches, couldn't start= 74\n",
      "current avg and SD of usage are 1.01119 0.14598\n",
      "starting to reduce usage for unit 1224 with usage 1.36096 . Total units tried = 8\n",
      "try to drop unit 1224 from 0 th HD out of 125 possible.  usage down to 1.3609612924263215\n",
      "try to drop unit 1224 from 30 th HD out of 125 possible.  usage down to 1.3609612924263215\n",
      "try to drop unit 1224 from 60 th HD out of 125 possible.  usage down to 1.3609612924263215\n",
      "try to drop unit 1224 from 90 th HD out of 125 possible.  usage down to 1.3176425710759874\n",
      "all done trying to reduce usage of unit 1224 final usage = 1.29162 6 sec elapsed 8 0 successful, failed patches, couldn't start= 92\n",
      "current avg and SD of usage are 1.01113 0.14585\n",
      "starting to reduce usage for unit 814 with usage 1.32961 . Total units tried = 9\n",
      "try to drop unit 814 from 0 th HD out of 90 possible.  usage down to 1.3296070290742559\n",
      "try to drop unit 814 from 30 th HD out of 90 possible.  usage down to 1.3183976911459059\n",
      "try to drop unit 814 from 60 th HD out of 90 possible.  usage down to 1.3183976911459059\n",
      "all done trying to reduce usage of unit 814 final usage = 1.3184 7 sec elapsed 1 0 successful, failed patches, couldn't start= 71\n",
      "current avg and SD of usage are 1.01112 0.14578\n",
      "starting to reduce usage for unit 3437 with usage 1.30278 . Total units tried = 10\n",
      "try to drop unit 3437 from 0 th HD out of 83 possible.  usage down to 1.3027750959193165\n",
      "try to drop unit 3437 from 30 th HD out of 83 possible.  usage down to 1.3027750959193165\n",
      "try to drop unit 3437 from 60 th HD out of 83 possible.  usage down to 1.292220195384989\n",
      "all done trying to reduce usage of unit 3437 final usage = 1.27859 7 sec elapsed 2 0 successful, failed patches, couldn't start= 65\n",
      "current avg and SD of usage are 1.01111 0.14558\n",
      "starting to reduce usage for unit 152 with usage 1.2985 . Total units tried = 11\n",
      "try to drop unit 152 from 0 th HD out of 61 possible.  usage down to 1.2984960821892806\n",
      "try to drop unit 152 from 30 th HD out of 61 possible.  usage down to 1.2394708633824545\n",
      "all done trying to reduce usage of unit 152 final usage = 1.19723 9 sec elapsed 8 0 successful, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.01105 0.14461\n",
      "starting to reduce usage for unit 439 with usage 1.28676 . Total units tried = 12\n",
      "try to drop unit 439 from 0 th HD out of 114 possible.  usage down to 1.2867581602119438\n",
      "try to drop unit 439 from 30 th HD out of 114 possible.  usage down to 1.2611511996151494\n",
      "try to drop unit 439 from 60 th HD out of 114 possible.  usage down to 1.2611511996151494\n",
      "try to drop unit 439 from 90 th HD out of 114 possible.  usage down to 1.2611511996151494\n",
      "all done trying to reduce usage of unit 439 final usage = 1.26115 10 sec elapsed 3 1 successful, failed patches, couldn't start= 88\n",
      "current avg and SD of usage are 1.01106 0.14451\n",
      "starting to reduce usage for unit 1801 with usage 1.2775 . Total units tried = 13\n",
      "try to drop unit 1801 from 0 th HD out of 118 possible.  usage down to 1.2774953540196663\n",
      "try to drop unit 1801 from 30 th HD out of 118 possible.  usage down to 1.2774953540196663\n",
      "try to drop unit 1801 from 60 th HD out of 118 possible.  usage down to 1.2774953540196663\n",
      "try to drop unit 1801 from 90 th HD out of 118 possible.  usage down to 1.2774953540196663\n",
      "all done trying to reduce usage of unit 1801 final usage = 1.2775 11 sec elapsed 0 0 successful, failed patches, couldn't start= 95\n",
      "current avg and SD of usage are 1.01106 0.14451\n",
      "starting to reduce usage for unit 514 with usage 1.26155 . Total units tried = 14\n",
      "try to drop unit 514 from 0 th HD out of 70 possible.  usage down to 1.2615455400960398\n",
      "try to drop unit 514 from 30 th HD out of 70 possible.  usage down to 1.2383046223853225\n",
      "all done trying to reduce usage of unit 514 final usage = 1.2383 11 sec elapsed 2 4 successful, failed patches, couldn't start= 50\n",
      "current avg and SD of usage are 1.01105 0.14438\n",
      "starting to reduce usage for unit 3130 with usage 1.25879 . Total units tried = 15\n",
      "try to drop unit 3130 from 0 th HD out of 103 possible.  usage down to 1.2587935469521376\n",
      "try to drop unit 3130 from 30 th HD out of 103 possible.  usage down to 1.2406538848255704\n",
      "try to drop unit 3130 from 60 th HD out of 103 possible.  usage down to 1.2406538848255704\n",
      "all done trying to reduce usage of unit 3130 final usage = 1.24065 11 sec elapsed 3 4 successful, failed patches, couldn't start= 76\n",
      "current avg and SD of usage are 1.01104 0.14428\n",
      "starting to reduce usage for unit 2489 with usage 1.25802 . Total units tried = 16\n",
      "try to drop unit 2489 from 0 th HD out of 66 possible.  usage down to 1.2580216464360088\n",
      "try to drop unit 2489 from 30 th HD out of 66 possible.  usage down to 1.220517349624846\n",
      "all done trying to reduce usage of unit 2489 final usage = 1.20687 13 sec elapsed 4 0 successful, failed patches, couldn't start= 49\n",
      "current avg and SD of usage are 1.01102 0.14347\n",
      "starting to reduce usage for unit 628 with usage 1.23907 . Total units tried = 17\n",
      "try to drop unit 628 from 0 th HD out of 28 possible.  usage down to 1.2390681326783317\n",
      "all done trying to reduce usage of unit 628 final usage = 1.23907 14 sec elapsed 0 0 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.01102 0.14347\n",
      "starting to reduce usage for unit 1713 with usage 1.22918 . Total units tried = 18\n",
      "try to drop unit 1713 from 0 th HD out of 48 possible.  usage down to 1.2291844499840843\n",
      "try to drop unit 1713 from 30 th HD out of 48 possible.  usage down to 1.195707460213043\n",
      "all done trying to reduce usage of unit 1713 final usage = 1.16927 14 sec elapsed 4 12 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.011 0.14293\n",
      "starting to reduce usage for unit 384 with usage 1.21214 . Total units tried = 19\n",
      "try to drop unit 384 from 0 th HD out of 94 possible.  usage down to 1.212143907070524\n",
      "try to drop unit 384 from 30 th HD out of 94 possible.  usage down to 1.212143907070524\n",
      "try to drop unit 384 from 60 th HD out of 94 possible.  usage down to 1.1969576034400524\n",
      "all done trying to reduce usage of unit 384 final usage = 1.18422 14 sec elapsed 2 1 successful, failed patches, couldn't start= 73\n",
      "current avg and SD of usage are 1.01101 0.14287\n",
      "starting to reduce usage for unit 1187 with usage 1.20527 . Total units tried = 20\n",
      "try to drop unit 1187 from 0 th HD out of 111 possible.  usage down to 1.2052723144334156\n",
      "try to drop unit 1187 from 30 th HD out of 111 possible.  usage down to 1.2052723144334156\n",
      "try to drop unit 1187 from 60 th HD out of 111 possible.  usage down to 1.2052723144334156\n",
      "all done trying to reduce usage of unit 1187 final usage = 1.20527 15 sec elapsed 0 0 successful, failed patches, couldn't start= 89\n",
      "current avg and SD of usage are 1.01101 0.14287\n",
      "starting to reduce usage for unit 1281 with usage 1.205 . Total units tried = 21\n",
      "try to drop unit 1281 from 0 th HD out of 82 possible.  usage down to 1.2049954370744458\n",
      "try to drop unit 1281 from 30 th HD out of 82 possible.  usage down to 1.1299700630061091\n",
      "try to drop unit 1281 from 60 th HD out of 82 possible.  usage down to 1.12040520878581\n",
      "all done trying to reduce usage of unit 1281 final usage = 1.09973 25 sec elapsed 17 12 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.01098 0.14189\n",
      "starting to reduce usage for unit 467 with usage 1.19144 . Total units tried = 22\n",
      "try to drop unit 467 from 0 th HD out of 101 possible.  usage down to 1.191436836705892\n",
      "try to drop unit 467 from 30 th HD out of 101 possible.  usage down to 1.191436836705892\n",
      "try to drop unit 467 from 60 th HD out of 101 possible.  usage down to 1.191436836705892\n",
      "all done trying to reduce usage of unit 467 final usage = 1.19144 25 sec elapsed 0 0 successful, failed patches, couldn't start= 81\n",
      "current avg and SD of usage are 1.01098 0.14189\n",
      "starting to reduce usage for unit 473 with usage 1.18499 . Total units tried = 23\n",
      "try to drop unit 473 from 0 th HD out of 39 possible.  usage down to 1.1849931454417304\n",
      "try to drop unit 473 from 30 th HD out of 39 possible.  usage down to 1.1622136899960231\n",
      "all done trying to reduce usage of unit 473 final usage = 1.16221 25 sec elapsed 2 3 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.01097 0.14164\n",
      "starting to reduce usage for unit 3058 with usage 1.17677 . Total units tried = 24\n",
      "try to drop unit 3058 from 0 th HD out of 94 possible.  usage down to 1.1767707269014422\n",
      "try to drop unit 3058 from 30 th HD out of 94 possible.  usage down to 1.1767707269014422\n",
      "try to drop unit 3058 from 60 th HD out of 94 possible.  usage down to 1.1767707269014422\n",
      "all done trying to reduce usage of unit 3058 final usage = 1.17677 26 sec elapsed 0 0 successful, failed patches, couldn't start= 76\n",
      "current avg and SD of usage are 1.01097 0.14164\n",
      "starting to reduce usage for unit 2220 with usage 1.15065 . Total units tried = 25\n",
      "try to drop unit 2220 from 0 th HD out of 86 possible.  usage down to 1.1506519627016654\n",
      "try to drop unit 2220 from 30 th HD out of 86 possible.  usage down to 1.128543725095979\n",
      "try to drop unit 2220 from 60 th HD out of 86 possible.  usage down to 1.128543725095979\n",
      "all done trying to reduce usage of unit 2220 final usage = 1.11643 26 sec elapsed 3 0 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.01095 0.14157\n",
      "starting to reduce usage for unit 1725 with usage 1.12225 . Total units tried = 26\n",
      "try to drop unit 1725 from 0 th HD out of 65 possible.  usage down to 1.1222510578458331\n",
      "try to drop unit 1725 from 30 th HD out of 65 possible.  usage down to 1.1222510578458331\n",
      "all done trying to reduce usage of unit 1725 final usage = 1.12225 26 sec elapsed 0 0 successful, failed patches, couldn't start= 52\n",
      "current avg and SD of usage are 1.01095 0.14157\n",
      "starting to reduce usage for unit 516 with usage 1.11723 . Total units tried = 27\n",
      "try to drop unit 516 from 0 th HD out of 56 possible.  usage down to 1.1172253142685988\n",
      "try to drop unit 516 from 30 th HD out of 56 possible.  usage down to 1.0518738673195667\n",
      "all done trying to reduce usage of unit 516 final usage = 1.01543 28 sec elapsed 7 11 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.01088 0.1413\n",
      "starting to reduce usage for unit 2158 with usage 1.10044 . Total units tried = 28\n",
      "all done trying to reduce usage of unit 2158 final usage = 1.10044 28 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01088 0.1413\n",
      "starting to reduce usage for unit 693 with usage 1.06555 . Total units tried = 29\n",
      "try to drop unit 693 from 0 th HD out of 67 possible.  usage down to 1.0655499308099716\n",
      "try to drop unit 693 from 30 th HD out of 67 possible.  usage down to 1.0553390294186558\n",
      "all done trying to reduce usage of unit 693 final usage = 1.05534 28 sec elapsed 1 0 successful, failed patches, couldn't start= 53\n",
      "current avg and SD of usage are 1.01088 0.14129\n",
      "starting to reduce usage for unit 769 with usage 1.06257 . Total units tried = 30\n",
      "try to drop unit 769 from 0 th HD out of 85 possible.  usage down to 1.062571401644961\n",
      "try to drop unit 769 from 30 th HD out of 85 possible.  usage down to 1.0560270277047563\n",
      "try to drop unit 769 from 60 th HD out of 85 possible.  usage down to 1.0560270277047563\n",
      "all done trying to reduce usage of unit 769 final usage = 1.05603 29 sec elapsed 1 1 successful, failed patches, couldn't start= 66\n",
      "current avg and SD of usage are 1.01088 0.14128\n",
      "starting to reduce usage for unit 1558 with usage 1.05831 . Total units tried = 31\n",
      "try to drop unit 1558 from 0 th HD out of 80 possible.  usage down to 1.0583091683608146\n",
      "try to drop unit 1558 from 30 th HD out of 80 possible.  usage down to 1.0583091683608146\n",
      "try to drop unit 1558 from 60 th HD out of 80 possible.  usage down to 1.0249160808197755\n",
      "all done trying to reduce usage of unit 1558 final usage = 1.02492 29 sec elapsed 3 0 successful, failed patches, couldn't start= 61\n",
      "current avg and SD of usage are 1.01086 0.14124\n",
      "starting to reduce usage for unit 3032 with usage 1.05468 . Total units tried = 32\n",
      "try to drop unit 3032 from 0 th HD out of 66 possible.  usage down to 1.0546845920246908\n",
      "try to drop unit 3032 from 30 th HD out of 66 possible.  usage down to 1.0388018998852029\n",
      "all done trying to reduce usage of unit 3032 final usage = 1.0388 30 sec elapsed 1 8 successful, failed patches, couldn't start= 44\n",
      "current avg and SD of usage are 1.01085 0.14122\n",
      "starting to reduce usage for unit 284 with usage 1.03971 . Total units tried = 33\n",
      "try to drop unit 284 from 0 th HD out of 61 possible.  usage down to 1.0397080439692585\n",
      "all done trying to reduce usage of unit 284 final usage = 1.0099 30 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01083 0.14121\n",
      "starting to reduce usage for unit 401 with usage 1.03863 . Total units tried = 34\n",
      "try to drop unit 401 from 0 th HD out of 86 possible.  usage down to 1.0386340954251516\n",
      "try to drop unit 401 from 30 th HD out of 86 possible.  usage down to 1.0203769701765806\n",
      "all done trying to reduce usage of unit 401 final usage = 1.00706 30 sec elapsed 3 0 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.01081 0.14121\n",
      "starting to reduce usage for unit 386 with usage 1.03785 . Total units tried = 35\n",
      "try to drop unit 386 from 0 th HD out of 59 possible.  usage down to 1.037845414463162\n",
      "try to drop unit 386 from 30 th HD out of 59 possible.  usage down to 1.0233303286727082\n",
      "all done trying to reduce usage of unit 386 final usage = 1.02333 31 sec elapsed 1 0 successful, failed patches, couldn't start= 47\n",
      "current avg and SD of usage are 1.01082 0.14119\n",
      "starting to reduce usage for unit 2036 with usage 1.0367 . Total units tried = 36\n",
      "try to drop unit 2036 from 0 th HD out of 87 possible.  usage down to 1.0366959539121419\n",
      "all done trying to reduce usage of unit 2036 final usage = 0.99697 31 sec elapsed 2 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01081 0.14119\n",
      "starting to reduce usage for unit 2192 with usage 1.03686 . Total units tried = 37\n",
      "try to drop unit 2192 from 0 th HD out of 80 possible.  usage down to 1.036863758372169\n",
      "all done trying to reduce usage of unit 2192 final usage = 1.00705 31 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01081 0.14119\n",
      "starting to reduce usage for unit 2659 with usage 1.03418 . Total units tried = 38\n",
      "try to drop unit 2659 from 0 th HD out of 78 possible.  usage down to 1.0341788870120519\n",
      "all done trying to reduce usage of unit 2659 final usage = 1.01992 31 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0108 0.14119\n",
      "starting to reduce usage for unit 1070 with usage 1.03097 . Total units tried = 39\n",
      "try to drop unit 1070 from 0 th HD out of 90 possible.  usage down to 1.0309738218259896\n",
      "all done trying to reduce usage of unit 1070 final usage = 1.01955 31 sec elapsed 1 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01079 0.14118\n",
      "starting to reduce usage for unit 1407 with usage 1.03024 . Total units tried = 40\n",
      "try to drop unit 1407 from 0 th HD out of 68 possible.  usage down to 1.0302438724249816\n",
      "all done trying to reduce usage of unit 1407 final usage = 1.00936 31 sec elapsed 3 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.01078 0.14118\n"
     ]
    }
   ],
   "source": [
    "print(\"we are still biased toward high overuse for large units, so do another round of overpatch\")\n",
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "maxSD = 0.08 #0.06  #0.08  #0.04 \n",
    "print(maxSD,\"= default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\")\n",
    "maxSD = float(input(\"enter maxSD\"))\n",
    "stopMaxUse = 1.02 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.15*aDP  #0.15 or 0.25 is debatable\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #\n",
    "maxGap = aDP - minDistrictPop  #0.9 * np.median(unitPop) # = aDP - minDistrictPop\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()\n",
    "for t in popHDlist:\n",
    "    if t not in hasSplitUnit: #hasSplitUnit[t] != 1:  #don't mess with the small number of HDs that have to have a split muni\n",
    "        nonfrozenList.append(t)\n",
    "        \n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #simplified vs. vanillaHD - just take the biggest overuser\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedBigUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = -999.  #preventing re-selection\n",
    "    OUU = eligUnitUse.index(np.max(eligUnitUse))\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    if unitPop[OUU] > 0.25 * aDP:  #some overusers are high-pop.  If so, will drop only this unit, not any neighbors\n",
    "        OUUc = [OUU]\n",
    "    else:\n",
    "        OUUc = [OUU] + unitNbrs[OUU]\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUUcSet = set( OUUc)  #for muni-snap, overused are isolated.  So don't bother adding two-level neighbors to cluster\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t] and homeU[t] != OUU: #can't drop the home muni\n",
    "            HDadjoinSet =   set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            OUUcAdjoinSet = set( getAdjoiners(list(OUUcSet),unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUUcAdjoinSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.8*len(ouuHDs) : #arbitrarily only examine 80% of HDs, biasing farthest ones       \n",
    "        if idxNo % 30 == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD out of\",len(ouuHDs),\"possible.  usage down to\",unitUse[OUU] )\n",
    "        idxNo -=1\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        thisLoopOUUc = [OUU]\n",
    "        possibleOUUc = list((  OUUcSet.intersection(set(HDunitList[t]) )  ).difference( {homeU[t]} )) #again, homeU is untouchable\n",
    "        if np.sum([unitPop[u] for u in possibleOUUc]) < 0.25*aDP:\n",
    "            thisLoopOUUc = possibleOUUc.copy() #we can add the whole non-HDincluded cluster (-homeU) without exceeding 0.25 aDP            \n",
    "        trySet = set(HDunitList[t]).difference(set(thisLoopOUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(thisLoopOUUc))  #set(OUUc)\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates and u != homeU[t]:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative) \n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "71c120cc-5cd5-442b-a7d6-09b5ca99f214",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 1.01078 and SD 0.14118\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n",
      "unitUse by unitPop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        patchedUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "\n",
    "re_overPatchedUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "plt.scatter(unitPop,patchedUse,label=\"patched\")\n",
    "#plt.scatter(unitPop,unpatchedUnitUse,label=\"unpatched\")\n",
    "print(\"unitUse by unitPop\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5191eba3-2394-4c90-bf85-315f44a8a4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "where are these overused units?\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"where are these overused units?\")\n",
    "overUseThresh = 1.2\n",
    "for u in range(nUnits):\n",
    "    plotPoly(unitGeom[u],0.1)\n",
    "    if unitUse[u] > overUseThresh:\n",
    "        plotPoly(unitGeom[u],1)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u])\n",
    "    if unitUse[u] < 0.8 :\n",
    "        plotCenter(\"u\",unitGeom[u],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b85bed03-5d07-4daa-9f37-a4d065c14291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideas: plot original bg-based fractional use for each of these.\n",
      "Drop unit in ranked order of least unitfrac, if legal and not a home u\n",
      "before doing so, estimate what the use would be after doing so\n",
      "if a restart, we need to recreate the unitFrac uses\n",
      "reading in the previously stored HD bg lists, snapped vtd-based HDs to bg's in OH_615COIsnap99\n",
      "of the 8941 total HDs, 8934 are non-empty\n"
     ]
    }
   ],
   "source": [
    "print(\"Ideas: plot original bg-based fractional use for each of these.\")\n",
    "print(\"Drop unit in ranked order of least unitfrac, if legal and not a home u\")\n",
    "print(\"before doing so, estimate what the use would be after doing so\")\n",
    "print(\"if a restart, we need to recreate the unitFrac uses\")\n",
    "print(\"reading in the previously stored HD bg lists, snapped vtd-based HDs to bg's in OH_615COIsnap99\")\n",
    "listDF = pd.read_csv(\"./2024state_HD_output/OH8941VRAbgLists.csv\")\n",
    "bgListString = listDF[\"HDbgList\"]\n",
    "HDwt = listDF[\"HDweight\"]\n",
    "HDweight = HDwt.copy()\n",
    "nHDs = len(bgListString)\n",
    "HDbgList = [ast.literal_eval(bgListString[h]) for h in range(nHDs) ]\n",
    "popHDlist = list()\n",
    "for h in range(nHDs):\n",
    "    if len(HDbgList[h]) > 0:\n",
    "        popHDlist.append(h)\n",
    "print(\"of the\",nHDs,\"total HDs,\",len(popHDlist),\"are non-empty\")\n",
    "\n",
    "unitFrac = [[0.]*nUnits for h in range(nHDs)]\n",
    "for h in popHDlist:\n",
    "    if h%1000 == 0:\n",
    "        print(\"computing unitFracs for HD\",h)\n",
    "    for b in HDbgList[h]:\n",
    "        if b not in skipList and tractUnitNo[b] >= 0: #protect against unassigned 0-pop blockgroups\n",
    "            u = tractUnitNo[b]\n",
    "            if unitPop[tractUnitNo[b]] == 0:                       \n",
    "                unitFrac[h][u] = 1.\n",
    "            else:\n",
    "                unitFrac[h][u] += tractPop[b]/unitPop[u]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5b8a6725-ba5e-40a7-b954-bb590346d3ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1387 pop/aDP 0.36 with use 1.73948 has 5 HDs that could drop it, total weight*nD= 0.011\n",
      "unit 3436 pop/aDP 0.708 with use 1.63772 has 1 HDs that could drop it, total weight*nD= 0.017\n",
      "unit 830 pop/aDP 0.524 with use 1.60743 has 5 HDs that could drop it, total weight*nD= 0.005\n",
      "unit 449 pop/aDP 0.727 with use 1.58769 has 11 HDs that could drop it, total weight*nD= 0.01\n",
      "unit 592 pop/aDP 0.212 with use 1.48322 has 1 HDs that could drop it, total weight*nD= 0.016\n",
      "unit 587 pop/aDP 0.566 with use 1.42411 has 0 HDs that could drop it, total weight*nD= 0.0\n",
      "unit 2557 pop/aDP 0.459 with use 1.38425 has 0 HDs that could drop it, total weight*nD= 0.0\n",
      "unit 814 pop/aDP 0.242 with use 1.3184 has 0 HDs that could drop it, total weight*nD= 0.0\n",
      "unit 1224 pop/aDP 0.077 with use 1.29162 has 0 HDs that could drop it, total weight*nD= 0.0\n",
      "unit 3437 pop/aDP 0.293 with use 1.27859 has 3 HDs that could drop it, total weight*nD= 0.012\n",
      "unit 1801 pop/aDP 0.075 with use 1.2775 has 1 HDs that could drop it, total weight*nD= 0.012\n",
      "unit 439 pop/aDP 0.195 with use 1.26115 has 0 HDs that could drop it, total weight*nD= 0.0\n",
      "unit 3130 pop/aDP 0.313 with use 1.24065 has 4 HDs that could drop it, total weight*nD= 0.007\n",
      "unit 628 pop/aDP 0.901 with use 1.23907 has 4 HDs that could drop it, total weight*nD= 0.011\n",
      "unit 514 pop/aDP 0.329 with use 1.2383 has 13 HDs that could drop it, total weight*nD= 0.007\n",
      "unit 2489 pop/aDP 0.583 with use 1.20687 has 1 HDs that could drop it, total weight*nD= 0.022\n",
      "unit 1187 pop/aDP 0.106 with use 1.20527 has 2 HDs that could drop it, total weight*nD= 0.011\n"
     ]
    }
   ],
   "source": [
    "useOrder = np.argsort(unitUse)\n",
    "useNo = -1\n",
    "while unitUse[useOrder[useNo]] > overUseThresh:\n",
    "    u = useOrder[useNo]\n",
    "    canDropList, canDropWt = list(), 0.\n",
    "    for t in popHDlist:\n",
    "        if u in HDunitList[t] and u != homeU[t]:\n",
    "            unbroken,noEnclave,__, ___ = enclaveCheck(list(set(HDunitList[t]).difference({u})), unitNbrs)\n",
    "            if unbroken and noEnclave:\n",
    "                canDropList.append(t)\n",
    "                canDropWt = HDweight[t]\n",
    "    print(\"unit\",u,\"pop/aDP\",r3(unitPop[u]/aDP),\"with use\",r5(unitUse[u]),\"has\",len(canDropList),\n",
    "          \"HDs that could drop it, total weight*nD=\",r3(canDropWt*nDistricts) )\n",
    "    useNo -=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "baeee116-9390-4409-bf16-19e1c40a1b9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, these are a small fraction of what we need.  We will need to regrow some HDs from scratch.\n",
      "For each overused unit, we will rank the HDs centered outside them by their bg-based unitFrac use.\n",
      " Working from least to most represented, we will then spawn-grow them, barring the overused unit.\n",
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "working avg precinct-to-HDcP distance for HD 6400\n",
      "working avg precinct-to-HDcP distance for HD 7200\n",
      "working avg precinct-to-HDcP distance for HD 8000\n",
      "working avg precinct-to-HDcP distance for HD 8800\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#12/14/24 - new for 1850COIsnap99\n",
    "print(\"OK, these are a small fraction of what we need.  We will need to regrow some HDs from scratch.\")\n",
    "print(\"For each overused unit, we will rank the HDs centered outside them by their bg-based unitFrac use.\")\n",
    "print(\" Working from least to most represented, we will then spawn-grow them, barring the overused unit.\")\n",
    "#needed for restart to compute scores in below block\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6907eb3b-ded9-474a-bc9f-21d9be4bc0b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1387 has 113 non-homeU HDs it could drop, with total weight 1.31721 vs current use 1.73947781285981\n",
      "  final use of unit 1387 is 1.042\n",
      "3436 has 89 non-homeU HDs it could drop, with total weight 0.93104 vs current use 1.637721188312906\n",
      "  final use of unit 3436 is 1.034\n",
      "830 has 85 non-homeU HDs it could drop, with total weight 1.04634 vs current use 1.6074324832817242\n",
      "  final use of unit 830 is 1.032\n",
      "449 has 77 non-homeU HDs it could drop, with total weight 0.94641 vs current use 1.5876902885622055\n",
      "  final use of unit 449 is 1.041\n",
      "592 has 86 non-homeU HDs it could drop, with total weight 1.22236 vs current use 1.4832236219859851\n",
      "  final use of unit 592 is 1.032\n",
      "587 has 87 non-homeU HDs it could drop, with total weight 0.85682 vs current use 1.4241061107261912\n",
      "  final use of unit 587 is 1.035\n",
      "2557 has 92 non-homeU HDs it could drop, with total weight 0.92509 vs current use 1.3842525514750113\n",
      "  final use of unit 2557 is 1.038\n",
      "814 has 89 non-homeU HDs it could drop, with total weight 1.06205 vs current use 1.3183976911459059\n",
      "  final use of unit 814 is 1.027\n",
      "1224 has 117 non-homeU HDs it could drop, with total weight 1.16933 vs current use 1.2916244895521738\n",
      "  final use of unit 1224 is 1.032\n",
      "3437 has 81 non-homeU HDs it could drop, with total weight 0.96889 vs current use 1.2785944732325643\n",
      "  final use of unit 3437 is 1.037\n"
     ]
    }
   ],
   "source": [
    "useOrder = np.argsort(unitUse)\n",
    "useNo = -1\n",
    "while unitUse[useOrder[useNo]] > overUseThresh:\n",
    "    OUU = useOrder[useNo]\n",
    "    canDropList, canDropScore, canDropWt = list(), list(), 0.\n",
    "    for t in popHDlist:\n",
    "        if OUU in HDunitList[t] and OUU != homeU[t]:\n",
    "            canDropList.append(t)\n",
    "            canDropScore.append(-1.*unitFrac[t][OUU])\n",
    "            canDropWt += nDistricts * HDweight[t]\n",
    "    nEligDrops = len(canDropList)\n",
    "    targetWtDrop,wtDropped = unitUse[OUU] - 1.04321, 0.  #don't push all the way to use=1\n",
    "    print(OUU,\"has\",nEligDrops,\"non-homeU HDs it could drop, with total weight\",r5(canDropWt),\"vs current use\",unitUse[OUU])\n",
    "    scoreIdx = np.argsort(canDropScore)\n",
    "    idxNo, barredUset = 0, {OUU}\n",
    "    while idxNo < nEligDrops and wtDropped < targetWtDrop:  #don't push all the way to use=1\n",
    "        t = canDropList[scoreIdx[idxNo]]\n",
    "        starterU = homeU[t]\n",
    "        contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "        contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(barredUset) )\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        #                                            subbed HDcircle for HDpoly in below\n",
    "        nearHDscore = [ 5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] for uu in adjoiners ]\n",
    "        stillGoing = True\n",
    "        \n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in barredUset:  \n",
    "                        nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                        nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        \n",
    "        wtDropped += nDistricts * HDweight[t]\n",
    "        idxNo +=1\n",
    "    useNo -=1\n",
    "    print(\"  final use of unit\",OUU,\"is\",r3(unitUse[OUU]) )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "64f5136f-b17a-4cc5-9a80-b0d67e5ac045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 1.00861 and SD 0.08546\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n",
      "unitUse by unitPop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        patchedUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "\n",
    "re_overPatchedUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "plt.scatter(unitPop,patchedUse,label=\"patched\")\n",
    "#plt.scatter(unitPop,unpatchedUnitUse,label=\"unpatched\")\n",
    "print(\"unitUse by unitPop\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "33b87417-a5cb-4ae1-84b5-cbd6bf9f825d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigPatchedList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c22368de-2d82-44c1-8067-535e452a1773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "much better, but let's do one more underpatch pass\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.055\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.055\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 696 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1078 units out of 3438 with usage below 0.98\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "Let's further tighten the distro with exchanges up to 29796 = aDP* 0.25\n",
      "current avg and SD of unit usage are 1.00861 0.08546 . Now trying to increase up to 400 units' underusage\n",
      "all done trying2increase usage of unit 2167 final usage = 0.48064 39 sec elapsed 2 125 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00862 0.08544\n",
      "all done trying2increase usage of unit 1349 final usage = 0.48591 69 sec elapsed 0 125 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00862 0.08544\n",
      "all done trying2increase usage of unit 1278 final usage = 0.50294 103 sec elapsed 1 123 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00862 0.08541\n",
      "all done trying2increase usage of unit 2168 final usage = 0.5163 136 sec elapsed 0 123 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00862 0.08541\n",
      "all done trying2increase usage of unit 2588 final usage = 0.53304 140 sec elapsed 2 7 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.00862 0.08538\n",
      "all done trying2increase usage of unit 2591 final usage = 0.54169 142 sec elapsed 1 6 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.00862 0.08535\n",
      "all done trying2increase usage of unit 1512 final usage = 0.55943 145 sec elapsed 3 6 complete, failed patches, unstarted= 33\n",
      "current avg and SD of usage are 1.00862 0.0853\n",
      "all done trying2increase usage of unit 1506 final usage = 0.55168 149 sec elapsed 1 8 complete, failed patches, unstarted= 57\n",
      "current avg and SD of usage are 1.00862 0.08528\n",
      "all done trying2increase usage of unit 1523 final usage = 0.56053 154 sec elapsed 2 17 complete, failed patches, unstarted= 58\n",
      "current avg and SD of usage are 1.00862 0.08524\n",
      "begin usage increase for unit 2076 with usage 0.55452 . Sec, total UU units tried = 154 10\n",
      "all done trying2increase usage of unit 2076 final usage = 0.98345 161 sec elapsed 48 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00865 0.08472\n",
      "all done trying2increase usage of unit 1853 final usage = 0.98793 166 sec elapsed 47 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00865 0.08419\n",
      "all done trying2increase usage of unit 1513 final usage = 0.5627 168 sec elapsed 1 5 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.00866 0.08418\n",
      "all done trying2increase usage of unit 1514 final usage = 0.5627 170 sec elapsed 0 5 complete, failed patches, unstarted= 36\n",
      "current avg and SD of usage are 1.00866 0.08418\n",
      "all done trying2increase usage of unit 946 final usage = 0.57135 172 sec elapsed 0 0 complete, failed patches, unstarted= 46\n",
      "current avg and SD of usage are 1.00866 0.08418\n",
      "all done trying2increase usage of unit 2343 final usage = 0.69211 183 sec elapsed 12 4 complete, failed patches, unstarted= 119\n",
      "current avg and SD of usage are 1.00866 0.08387\n",
      "all done trying2increase usage of unit 1495 final usage = 0.60926 188 sec elapsed 4 19 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.00866 0.08382\n",
      "all done trying2increase usage of unit 755 final usage = 0.98839 201 sec elapsed 43 22 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00871 0.08357\n",
      "all done trying2increase usage of unit 1511 final usage = 0.61544 203 sec elapsed 1 11 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.00871 0.08356\n",
      "all done trying2increase usage of unit 1504 final usage = 0.61544 205 sec elapsed 0 1 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.00871 0.08356\n",
      "begin usage increase for unit 1510 with usage 0.61544 . Sec, total UU units tried = 205 20\n",
      "all done trying2increase usage of unit 1510 final usage = 0.62731 207 sec elapsed 1 10 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.00871 0.08351\n",
      "all done trying2increase usage of unit 1507 final usage = 0.64528 212 sec elapsed 1 11 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.00872 0.08343\n",
      "all done trying2increase usage of unit 858 final usage = 0.68364 230 sec elapsed 4 85 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00873 0.08313\n",
      "all done trying2increase usage of unit 920 final usage = 0.98615 241 sec elapsed 30 80 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.0088 0.08156\n",
      "all done trying2increase usage of unit 1488 final usage = 0.64823 245 sec elapsed 0 6 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.0088 0.08156\n",
      "all done trying2increase usage of unit 2575 final usage = 0.64918 251 sec elapsed 0 24 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.0088 0.08156\n",
      "all done trying2increase usage of unit 730 final usage = 0.9235 278 sec elapsed 35 47 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00884 0.08107\n",
      "all done trying2increase usage of unit 3111 final usage = 0.9267 316 sec elapsed 27 80 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.0089 0.08057\n",
      "all done trying2increase usage of unit 2924 final usage = 0.98587 327 sec elapsed 32 27 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00892 0.0798\n",
      "all done trying2increase usage of unit 1723 final usage = 0.98218 330 sec elapsed 28 8 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.00896 0.07954\n",
      "begin usage increase for unit 2611 with usage 0.67968 . Sec, total UU units tried = 331 30\n",
      "all done trying2increase usage of unit 2611 final usage = 0.97204 337 sec elapsed 22 56 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.009 0.07943\n",
      "all done trying2increase usage of unit 557 final usage = 0.77398 379 sec elapsed 10 104 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.00902 0.07927\n",
      "all done trying2increase usage of unit 2105 final usage = 0.80087 383 sec elapsed 13 29 complete, failed patches, unstarted= 111\n",
      "current avg and SD of usage are 1.00904 0.07912\n",
      "all done trying2increase usage of unit 479 final usage = 0.74886 388 sec elapsed 7 30 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.00904 0.07888\n",
      "all done trying2increase usage of unit 1731 final usage = 0.7575 391 sec elapsed 5 6 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.00903 0.07877\n",
      "all done trying2increase usage of unit 498 final usage = 0.7755 406 sec elapsed 9 15 complete, failed patches, unstarted= 104\n",
      "current avg and SD of usage are 1.00903 0.07866\n",
      "all done trying2increase usage of unit 3020 final usage = 0.99499 419 sec elapsed 21 18 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.00904 0.07785\n",
      "all done trying2increase usage of unit 93 final usage = 0.86121 433 sec elapsed 16 26 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.00909 0.0773\n",
      "all done trying2increase usage of unit 190 final usage = 0.83032 445 sec elapsed 13 40 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.00911 0.07715\n",
      "all done trying2increase usage of unit 3112 final usage = 0.90062 464 sec elapsed 14 17 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.00913 0.07687\n",
      "begin usage increase for unit 700 with usage 0.71534 . Sec, total UU units tried = 465 40\n",
      "all done trying2increase usage of unit 700 final usage = 0.97064 540 sec elapsed 32 127 complete, failed patches, unstarted= 44\n",
      "current avg and SD of usage are 1.00913 0.07637\n",
      "all done trying2increase usage of unit 551 final usage = 0.83588 545 sec elapsed 10 75 complete, failed patches, unstarted= 100\n",
      "current avg and SD of usage are 1.00916 0.07561\n",
      "all done trying2increase usage of unit 756 final usage = 0.98392 565 sec elapsed 29 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00915 0.07508\n",
      "all done trying2increase usage of unit 738 final usage = 0.83209 567 sec elapsed 10 23 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.00918 0.07462\n",
      "all done trying2increase usage of unit 1242 final usage = 0.99049 584 sec elapsed 16 21 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0092 0.07391\n",
      "all done trying2increase usage of unit 1500 final usage = 0.74035 588 sec elapsed 1 10 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.00921 0.0739\n",
      "all done trying2increase usage of unit 2709 final usage = 0.98248 596 sec elapsed 24 12 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.00917 0.07361\n",
      "all done trying2increase usage of unit 566 final usage = 0.75355 606 sec elapsed 2 21 complete, failed patches, unstarted= 70\n",
      "current avg and SD of usage are 1.00918 0.0735\n",
      "all done trying2increase usage of unit 104 final usage = 0.80586 659 sec elapsed 4 100 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.00919 0.07337\n",
      "all done trying2increase usage of unit 95 final usage = 0.7944 710 sec elapsed 4 95 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.00919 0.07329\n",
      "begin usage increase for unit 790 with usage 0.73754 . Sec, total UU units tried = 710 50\n",
      "all done trying2increase usage of unit 790 final usage = 0.7636 721 sec elapsed 3 21 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.00919 0.0732\n",
      "all done trying2increase usage of unit 1384 final usage = 0.98077 731 sec elapsed 22 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00922 0.07293\n",
      "all done trying2increase usage of unit 2932 final usage = 0.78548 765 sec elapsed 4 88 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.00923 0.07275\n",
      "all done trying2increase usage of unit 789 final usage = 0.77093 766 sec elapsed 2 15 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00924 0.07262\n",
      "all done trying2increase usage of unit 2133 final usage = 0.74914 767 sec elapsed 0 2 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.00924 0.07262\n",
      "all done trying2increase usage of unit 2365 final usage = 0.80659 776 sec elapsed 6 9 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.00924 0.07253\n",
      "all done trying2increase usage of unit 2850 final usage = 0.98273 785 sec elapsed 25 20 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00928 0.0723\n",
      "all done trying2increase usage of unit 2492 final usage = 0.79059 790 sec elapsed 4 30 complete, failed patches, unstarted= 113\n",
      "current avg and SD of usage are 1.00928 0.07229\n",
      "all done trying2increase usage of unit 32 final usage = 0.87272 799 sec elapsed 10 27 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.0093 0.07213\n",
      "all done trying2increase usage of unit 1515 final usage = 0.76347 803 sec elapsed 1 12 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.0093 0.07211\n",
      "begin usage increase for unit 1502 with usage 0.7632 . Sec, total UU units tried = 803 60\n",
      "all done trying2increase usage of unit 1502 final usage = 0.77522 815 sec elapsed 1 45 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0093 0.07209\n",
      "all done trying2increase usage of unit 1522 final usage = 0.7896 833 sec elapsed 3 63 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.0093 0.07203\n",
      "all done trying2increase usage of unit 1650 final usage = 0.98951 844 sec elapsed 19 1 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.00932 0.07187\n",
      "all done trying2increase usage of unit 1865 final usage = 0.99117 851 sec elapsed 16 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00932 0.07159\n",
      "all done trying2increase usage of unit 2335 final usage = 0.82062 902 sec elapsed 6 81 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.00933 0.07155\n",
      "all done trying2increase usage of unit 1493 final usage = 0.79726 905 sec elapsed 5 6 complete, failed patches, unstarted= 65\n",
      "current avg and SD of usage are 1.00934 0.07151\n",
      "all done trying2increase usage of unit 2942 final usage = 0.93554 924 sec elapsed 17 93 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.00934 0.07141\n",
      "all done trying2increase usage of unit 1317 final usage = 0.86736 933 sec elapsed 11 50 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.00935 0.07129\n",
      "all done trying2increase usage of unit 3193 final usage = 0.79812 954 sec elapsed 3 46 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.00937 0.07124\n",
      "all done trying2increase usage of unit 3235 final usage = 0.79934 972 sec elapsed 2 50 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.00937 0.07121\n",
      "begin usage increase for unit 1788 with usage 0.77468 . Sec, total UU units tried = 972 70\n",
      "all done trying2increase usage of unit 1788 final usage = 0.79017 974 sec elapsed 1 16 complete, failed patches, unstarted= 58\n",
      "current avg and SD of usage are 1.00938 0.07115\n",
      "all done trying2increase usage of unit 1844 final usage = 0.7783 982 sec elapsed 0 12 complete, failed patches, unstarted= 29\n",
      "current avg and SD of usage are 1.00938 0.07115\n",
      "all done trying2increase usage of unit 3159 final usage = 0.81906 985 sec elapsed 4 9 complete, failed patches, unstarted= 132\n",
      "current avg and SD of usage are 1.00938 0.07112\n",
      "all done trying2increase usage of unit 1757 final usage = 0.98016 990 sec elapsed 23 21 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.00937 0.07097\n",
      "all done trying2increase usage of unit 2620 final usage = 0.98892 992 sec elapsed 15 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00938 0.0709\n",
      "all done trying2increase usage of unit 1735 final usage = 0.79285 995 sec elapsed 1 9 complete, failed patches, unstarted= 47\n",
      "current avg and SD of usage are 1.00938 0.07083\n",
      "all done trying2increase usage of unit 468 final usage = 0.98958 996 sec elapsed 14 3 complete, failed patches, unstarted= 19\n",
      "current avg and SD of usage are 1.00935 0.07033\n",
      "all done trying2increase usage of unit 983 final usage = 0.80829 1003 sec elapsed 4 20 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.00935 0.07029\n",
      "all done trying2increase usage of unit 662 final usage = 0.98595 1010 sec elapsed 17 18 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00934 0.06996\n",
      "all done trying2increase usage of unit 1498 final usage = 0.79259 1019 sec elapsed 1 27 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.00934 0.06995\n",
      "begin usage increase for unit 1239 with usage 0.78973 . Sec, total UU units tried = 1019 80\n",
      "all done trying2increase usage of unit 1239 final usage = 0.96937 1032 sec elapsed 16 55 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.00935 0.06978\n",
      "all done trying2increase usage of unit 966 final usage = 0.78996 1036 sec elapsed 0 7 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.00935 0.06978\n",
      "all done trying2increase usage of unit 2977 final usage = 0.79948 1069 sec elapsed 1 69 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.00935 0.06976\n",
      "all done trying2increase usage of unit 2177 final usage = 0.7907 1088 sec elapsed 0 71 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.00935 0.06976\n",
      "all done trying2increase usage of unit 1491 final usage = 0.80375 1097 sec elapsed 1 22 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.00935 0.06974\n",
      "all done trying2increase usage of unit 2004 final usage = 0.79129 1098 sec elapsed 0 15 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00935 0.06974\n",
      "all done trying2increase usage of unit 1843 final usage = 0.79132 1115 sec elapsed 0 50 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.00935 0.06974\n",
      "all done trying2increase usage of unit 1782 final usage = 0.98132 1117 sec elapsed 17 9 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.00941 0.06896\n",
      "all done trying2increase usage of unit 2421 final usage = 0.89076 1119 sec elapsed 11 10 complete, failed patches, unstarted= 78\n",
      "current avg and SD of usage are 1.00944 0.06862\n",
      "all done trying2increase usage of unit 2066 final usage = 0.90244 1136 sec elapsed 9 75 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00947 0.06857\n",
      "begin usage increase for unit 1463 with usage 0.79362 . Sec, total UU units tried = 1136 90\n",
      "all done trying2increase usage of unit 1463 final usage = 0.79362 1140 sec elapsed 0 18 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.00947 0.06857\n",
      "all done trying2increase usage of unit 1063 final usage = 0.9855 1178 sec elapsed 22 9 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.00949 0.06847\n",
      "all done trying2increase usage of unit 1760 final usage = 0.87147 1185 sec elapsed 7 14 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.0095 0.0684\n",
      "all done trying2increase usage of unit 1732 final usage = 0.99126 1205 sec elapsed 18 4 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 1.00953 0.06821\n",
      "all done trying2increase usage of unit 3103 final usage = 0.98276 1237 sec elapsed 17 4 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 1.00952 0.06808\n",
      "all done trying2increase usage of unit 3104 final usage = 0.98835 1266 sec elapsed 18 6 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.00952 0.06805\n",
      "all done trying2increase usage of unit 971 final usage = 0.86606 1274 sec elapsed 5 126 complete, failed patches, unstarted= 119\n",
      "current avg and SD of usage are 1.00952 0.06796\n",
      "all done trying2increase usage of unit 2987 final usage = 0.81278 1276 sec elapsed 1 7 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.00952 0.06794\n",
      "all done trying2increase usage of unit 1711 final usage = 0.92824 1281 sec elapsed 10 36 complete, failed patches, unstarted= 89\n",
      "current avg and SD of usage are 1.00954 0.06788\n",
      "all done trying2increase usage of unit 86 final usage = 0.81808 1328 sec elapsed 3 82 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.00954 0.06784\n",
      "begin usage increase for unit 1749 with usage 0.80231 . Sec, total UU units tried = 1328 100\n",
      "all done trying2increase usage of unit 1749 final usage = 0.98658 1330 sec elapsed 14 17 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00955 0.06778\n",
      "all done trying2increase usage of unit 2297 final usage = 0.80958 1338 sec elapsed 1 9 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 1.00955 0.06776\n",
      "all done trying2increase usage of unit 2166 final usage = 0.81723 1353 sec elapsed 1 41 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.00955 0.06776\n",
      "all done trying2increase usage of unit 1830 final usage = 0.85044 1354 sec elapsed 5 11 complete, failed patches, unstarted= 57\n",
      "current avg and SD of usage are 1.00954 0.06752\n",
      "all done trying2increase usage of unit 3189 final usage = 0.92475 1378 sec elapsed 12 67 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.00957 0.06739\n",
      "all done trying2increase usage of unit 1435 final usage = 0.82028 1385 sec elapsed 1 15 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.00957 0.06737\n",
      "all done trying2increase usage of unit 1456 final usage = 0.81957 1386 sec elapsed 2 7 complete, failed patches, unstarted= 48\n",
      "current avg and SD of usage are 1.00957 0.06734\n",
      "all done trying2increase usage of unit 1524 final usage = 0.82233 1390 sec elapsed 1 9 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.00957 0.06732\n",
      "all done trying2increase usage of unit 3110 final usage = 0.83816 1396 sec elapsed 2 27 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.00957 0.06725\n",
      "all done trying2increase usage of unit 605 final usage = 0.85816 1424 sec elapsed 4 74 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.0096 0.06719\n",
      "begin usage increase for unit 3044 with usage 0.81278 . Sec, total UU units tried = 1424 110\n",
      "all done trying2increase usage of unit 3044 final usage = 0.87126 1429 sec elapsed 7 34 complete, failed patches, unstarted= 59\n",
      "current avg and SD of usage are 1.0096 0.06714\n",
      "all done trying2increase usage of unit 1544 final usage = 0.84909 1431 sec elapsed 3 12 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.0096 0.06704\n",
      "all done trying2increase usage of unit 3067 final usage = 0.98966 1433 sec elapsed 14 2 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00962 0.06701\n",
      "all done trying2increase usage of unit 1704 final usage = 0.8145 1445 sec elapsed 0 81 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00962 0.06701\n",
      "all done trying2increase usage of unit 978 final usage = 0.98334 1453 sec elapsed 15 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00961 0.06687\n",
      "all done trying2increase usage of unit 2367 final usage = 0.83578 1459 sec elapsed 2 7 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.00961 0.06684\n",
      "all done trying2increase usage of unit 2366 final usage = 0.81663 1462 sec elapsed 0 6 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.00961 0.06684\n",
      "all done trying2increase usage of unit 866 final usage = 0.98029 1468 sec elapsed 14 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00963 0.06659\n",
      "all done trying2increase usage of unit 1038 final usage = 0.98369 1477 sec elapsed 14 7 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00968 0.06645\n",
      "all done trying2increase usage of unit 1254 final usage = 0.87129 1485 sec elapsed 6 87 complete, failed patches, unstarted= 81\n",
      "current avg and SD of usage are 1.00968 0.06639\n",
      "begin usage increase for unit 2253 with usage 0.82007 . Sec, total UU units tried = 1485 120\n",
      "all done trying2increase usage of unit 2253 final usage = 0.84942 1496 sec elapsed 3 34 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.00969 0.06638\n",
      "all done trying2increase usage of unit 1351 final usage = 0.82989 1528 sec elapsed 1 68 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.00969 0.06636\n",
      "all done trying2increase usage of unit 1250 final usage = 0.82213 1529 sec elapsed 0 15 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.00969 0.06636\n",
      "all done trying2increase usage of unit 1577 final usage = 0.84812 1558 sec elapsed 3 51 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.00969 0.06634\n",
      "all done trying2increase usage of unit 988 final usage = 0.98062 1565 sec elapsed 13 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00968 0.066\n",
      "all done trying2increase usage of unit 184 final usage = 0.9825 1570 sec elapsed 15 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00969 0.06593\n",
      "all done trying2increase usage of unit 1520 final usage = 0.84318 1580 sec elapsed 2 29 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.00971 0.06592\n",
      "all done trying2increase usage of unit 1273 final usage = 0.98803 1583 sec elapsed 12 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00971 0.06578\n",
      "all done trying2increase usage of unit 1083 final usage = 0.98328 1606 sec elapsed 17 24 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00971 0.06566\n",
      "all done trying2increase usage of unit 3271 final usage = 0.93751 1671 sec elapsed 11 88 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.00975 0.06555\n",
      "begin usage increase for unit 1528 with usage 0.82818 . Sec, total UU units tried = 1671 130\n",
      "all done trying2increase usage of unit 1528 final usage = 0.98174 1677 sec elapsed 20 13 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.00978 0.06538\n",
      "all done trying2increase usage of unit 1718 final usage = 0.98355 1696 sec elapsed 15 34 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0098 0.0653\n",
      "all done trying2increase usage of unit 758 final usage = 0.87122 1706 sec elapsed 5 19 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.00981 0.06526\n",
      "all done trying2increase usage of unit 1184 final usage = 0.98923 1710 sec elapsed 14 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00978 0.06513\n",
      "all done trying2increase usage of unit 1516 final usage = 0.83107 1715 sec elapsed 0 12 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.00978 0.06513\n",
      "all done trying2increase usage of unit 910 final usage = 0.83209 1717 sec elapsed 0 12 complete, failed patches, unstarted= 86\n",
      "current avg and SD of usage are 1.00978 0.06513\n",
      "all done trying2increase usage of unit 718 final usage = 0.83277 1718 sec elapsed 0 15 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.00978 0.06513\n",
      "all done trying2increase usage of unit 2144 final usage = 0.87252 1743 sec elapsed 3 46 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.00978 0.06508\n",
      "all done trying2increase usage of unit 630 final usage = 0.88556 1764 sec elapsed 4 43 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.00979 0.06501\n",
      "all done trying2increase usage of unit 2420 final usage = 0.83838 1768 sec elapsed 0 23 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.00979 0.06501\n",
      "begin usage increase for unit 990 with usage 0.83882 . Sec, total UU units tried = 1768 140\n",
      "all done trying2increase usage of unit 990 final usage = 0.86986 1773 sec elapsed 4 25 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.0098 0.06493\n",
      "all done trying2increase usage of unit 2329 final usage = 0.83951 1789 sec elapsed 0 30 complete, failed patches, unstarted= 75\n",
      "current avg and SD of usage are 1.0098 0.06493\n",
      "all done trying2increase usage of unit 2451 final usage = 0.86057 1839 sec elapsed 3 89 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.0098 0.06492\n",
      "all done trying2increase usage of unit 539 final usage = 0.98187 1843 sec elapsed 8 28 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.00979 0.06491\n",
      "all done trying2increase usage of unit 2986 final usage = 0.8629 1847 sec elapsed 2 25 complete, failed patches, unstarted= 70\n",
      "current avg and SD of usage are 1.0098 0.06488\n",
      "all done trying2increase usage of unit 1842 final usage = 0.92482 1893 sec elapsed 8 109 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.00982 0.06483\n",
      "all done trying2increase usage of unit 1716 final usage = 0.87404 1930 sec elapsed 3 122 complete, failed patches, unstarted= 56\n",
      "current avg and SD of usage are 1.00984 0.0648\n",
      "all done trying2increase usage of unit 2549 final usage = 0.98927 1938 sec elapsed 11 24 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.00986 0.06477\n",
      "all done trying2increase usage of unit 274 final usage = 0.98354 1946 sec elapsed 17 1 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.00988 0.06474\n",
      "all done trying2increase usage of unit 203 final usage = 0.9822 1964 sec elapsed 11 54 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.00994 0.06467\n",
      "begin usage increase for unit 1696 with usage 0.84468 . Sec, total UU units tried = 1964 150\n",
      "all done trying2increase usage of unit 1696 final usage = 0.98904 1966 sec elapsed 14 4 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.00995 0.06434\n",
      "all done trying2increase usage of unit 489 final usage = 0.98386 1974 sec elapsed 18 23 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.00998 0.06423\n",
      "all done trying2increase usage of unit 1209 final usage = 0.84537 1999 sec elapsed 0 55 complete, failed patches, unstarted= 219\n",
      "current avg and SD of usage are 1.00998 0.06423\n",
      "all done trying2increase usage of unit 1689 final usage = 0.98256 2003 sec elapsed 12 35 complete, failed patches, unstarted= 30\n",
      "current avg and SD of usage are 1.01005 0.06391\n",
      "all done trying2increase usage of unit 103 final usage = 0.87453 2014 sec elapsed 3 25 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01005 0.06389\n",
      "all done trying2increase usage of unit 39 final usage = 0.98289 2019 sec elapsed 10 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01005 0.06378\n",
      "all done trying2increase usage of unit 1900 final usage = 0.98819 2022 sec elapsed 13 6 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01005 0.0637\n",
      "all done trying2increase usage of unit 2272 final usage = 0.98721 2027 sec elapsed 12 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01002 0.06358\n",
      "all done trying2increase usage of unit 1503 final usage = 0.84767 2034 sec elapsed 0 21 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.01002 0.06358\n",
      "all done trying2increase usage of unit 2032 final usage = 0.99115 2049 sec elapsed 12 36 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01004 0.06342\n",
      "begin usage increase for unit 2324 with usage 0.84839 . Sec, total UU units tried = 2049 160\n",
      "all done trying2increase usage of unit 2324 final usage = 0.85488 2052 sec elapsed 1 5 complete, failed patches, unstarted= 31\n",
      "current avg and SD of usage are 1.01005 0.06341\n",
      "all done trying2increase usage of unit 3026 final usage = 0.8633 2058 sec elapsed 2 20 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.01005 0.06339\n",
      "all done trying2increase usage of unit 2703 final usage = 0.98372 2067 sec elapsed 14 42 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01008 0.06329\n",
      "all done trying2increase usage of unit 1467 final usage = 0.84897 2068 sec elapsed 0 7 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.01008 0.06329\n",
      "all done trying2increase usage of unit 503 final usage = 0.86238 2071 sec elapsed 1 28 complete, failed patches, unstarted= 42\n",
      "current avg and SD of usage are 1.01009 0.06325\n",
      "all done trying2increase usage of unit 3225 final usage = 0.89951 2144 sec elapsed 6 108 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01009 0.06321\n",
      "all done trying2increase usage of unit 1518 final usage = 0.8659 2158 sec elapsed 2 58 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01009 0.06308\n",
      "all done trying2increase usage of unit 1452 final usage = 0.85216 2164 sec elapsed 0 14 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.01009 0.06308\n",
      "all done trying2increase usage of unit 1908 final usage = 0.99272 2187 sec elapsed 12 58 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.01013 0.06304\n",
      "all done trying2increase usage of unit 1192 final usage = 0.99396 2196 sec elapsed 15 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01012 0.06282\n",
      "begin usage increase for unit 1914 with usage 0.85514 . Sec, total UU units tried = 2196 170\n",
      "all done trying2increase usage of unit 1914 final usage = 0.98482 2216 sec elapsed 13 45 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.01017 0.06276\n",
      "all done trying2increase usage of unit 2012 final usage = 0.98609 2219 sec elapsed 10 1 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01016 0.06271\n",
      "all done trying2increase usage of unit 661 final usage = 0.98841 2225 sec elapsed 10 98 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01015 0.06269\n",
      "all done trying2increase usage of unit 72 final usage = 0.87294 2235 sec elapsed 2 35 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.01015 0.06268\n",
      "all done trying2increase usage of unit 2245 final usage = 0.98251 2236 sec elapsed 12 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01015 0.06262\n",
      "all done trying2increase usage of unit 1499 final usage = 0.87414 2247 sec elapsed 2 40 complete, failed patches, unstarted= 19\n",
      "current avg and SD of usage are 1.01016 0.06261\n",
      "all done trying2increase usage of unit 3392 final usage = 0.98562 2259 sec elapsed 14 10 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.01017 0.06248\n",
      "all done trying2increase usage of unit 635 final usage = 0.98757 2261 sec elapsed 11 102 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01018 0.06239\n",
      "all done trying2increase usage of unit 115 final usage = 0.92797 2300 sec elapsed 6 98 complete, failed patches, unstarted= 69\n",
      "current avg and SD of usage are 1.0102 0.06225\n",
      "all done trying2increase usage of unit 1685 final usage = 1.00615 2307 sec elapsed 8 7 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.01025 0.06225\n",
      "begin usage increase for unit 450 with usage 0.85887 . Sec, total UU units tried = 2307 180\n",
      "all done trying2increase usage of unit 450 final usage = 0.89344 2360 sec elapsed 4 110 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.01026 0.06218\n",
      "all done trying2increase usage of unit 3056 final usage = 0.98449 2368 sec elapsed 14 53 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.01027 0.06212\n",
      "all done trying2increase usage of unit 931 final usage = 0.98495 2371 sec elapsed 13 20 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.01028 0.06207\n",
      "all done trying2increase usage of unit 1845 final usage = 0.99016 2379 sec elapsed 10 30 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01029 0.06202\n",
      "all done trying2increase usage of unit 1487 final usage = 0.8633 2385 sec elapsed 0 18 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.01029 0.06202\n",
      "all done trying2increase usage of unit 1470 final usage = 0.90494 2387 sec elapsed 4 5 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.0103 0.06195\n",
      "all done trying2increase usage of unit 2212 final usage = 0.98611 2395 sec elapsed 9 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01031 0.06193\n",
      "all done trying2increase usage of unit 2466 final usage = 0.98838 2399 sec elapsed 12 7 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01031 0.06176\n",
      "all done trying2increase usage of unit 3417 final usage = 0.98072 2407 sec elapsed 11 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01032 0.06171\n",
      "all done trying2increase usage of unit 1543 final usage = 0.86942 2411 sec elapsed 0 7 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.01032 0.06171\n",
      "begin usage increase for unit 2289 with usage 0.8706 . Sec, total UU units tried = 2411 190\n",
      "all done trying2increase usage of unit 2289 final usage = 0.94186 2460 sec elapsed 6 104 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01033 0.06168\n",
      "all done trying2increase usage of unit 3166 final usage = 0.92779 2463 sec elapsed 4 97 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01034 0.06163\n",
      "all done trying2increase usage of unit 1755 final usage = 0.98475 2473 sec elapsed 11 48 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01036 0.06156\n",
      "all done trying2increase usage of unit 1519 final usage = 0.8712 2480 sec elapsed 0 12 complete, failed patches, unstarted= 58\n",
      "current avg and SD of usage are 1.01036 0.06156\n",
      "all done trying2increase usage of unit 2514 final usage = 0.99159 2484 sec elapsed 7 39 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01036 0.06154\n",
      "all done trying2increase usage of unit 1548 final usage = 0.98062 2503 sec elapsed 10 60 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.01039 0.06151\n",
      "all done trying2increase usage of unit 3369 final usage = 0.94731 2540 sec elapsed 8 75 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01041 0.06146\n",
      "all done trying2increase usage of unit 2643 final usage = 0.98961 2542 sec elapsed 10 2 complete, failed patches, unstarted= 48\n",
      "current avg and SD of usage are 1.01042 0.06143\n",
      "all done trying2increase usage of unit 2461 final usage = 0.88411 2546 sec elapsed 1 41 complete, failed patches, unstarted= 110\n",
      "current avg and SD of usage are 1.01042 0.06141\n",
      "all done trying2increase usage of unit 2124 final usage = 0.9296 2559 sec elapsed 7 52 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.01045 0.06128\n",
      "begin usage increase for unit 2323 with usage 0.87436 . Sec, total UU units tried = 2559 200\n",
      "all done trying2increase usage of unit 2323 final usage = 0.88604 2562 sec elapsed 2 4 complete, failed patches, unstarted= 36\n",
      "current avg and SD of usage are 1.01045 0.06126\n",
      "all done trying2increase usage of unit 631 final usage = 0.99272 2565 sec elapsed 10 6 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.01046 0.06115\n",
      "all done trying2increase usage of unit 2351 final usage = 0.90206 2579 sec elapsed 3 18 complete, failed patches, unstarted= 68\n",
      "current avg and SD of usage are 1.01047 0.06113\n",
      "all done trying2increase usage of unit 2061 final usage = 0.98956 2582 sec elapsed 10 16 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01045 0.06108\n",
      "all done trying2increase usage of unit 1359 final usage = 0.88767 2585 sec elapsed 1 31 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01045 0.06107\n",
      "all done trying2increase usage of unit 2841 final usage = 0.9315 2588 sec elapsed 5 30 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.01048 0.06104\n",
      "all done trying2increase usage of unit 553 final usage = 0.98804 2593 sec elapsed 10 12 complete, failed patches, unstarted= 88\n",
      "current avg and SD of usage are 1.01051 0.06101\n",
      "all done trying2increase usage of unit 2984 final usage = 0.98817 2598 sec elapsed 9 10 complete, failed patches, unstarted= 67\n",
      "current avg and SD of usage are 1.01051 0.061\n",
      "all done trying2increase usage of unit 2395 final usage = 0.87994 2600 sec elapsed 0 5 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.01051 0.061\n",
      "all done trying2increase usage of unit 3068 final usage = 0.98289 2605 sec elapsed 8 9 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.01051 0.06093\n",
      "begin usage increase for unit 1432 with usage 0.88183 . Sec, total UU units tried = 2605 210\n",
      "all done trying2increase usage of unit 1432 final usage = 0.9921 2613 sec elapsed 13 54 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01054 0.06076\n",
      "all done trying2increase usage of unit 2120 final usage = 0.98679 2615 sec elapsed 7 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01055 0.06073\n",
      "all done trying2increase usage of unit 3013 final usage = 0.98353 2617 sec elapsed 6 11 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01057 0.0607\n",
      "all done trying2increase usage of unit 1307 final usage = 0.98905 2640 sec elapsed 11 26 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01062 0.06062\n",
      "all done trying2increase usage of unit 2183 final usage = 0.88432 2643 sec elapsed 0 9 complete, failed patches, unstarted= 24\n",
      "current avg and SD of usage are 1.01062 0.06062\n",
      "all done trying2increase usage of unit 241 final usage = 0.98643 2646 sec elapsed 8 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01062 0.0606\n",
      "all done trying2increase usage of unit 1541 final usage = 0.89797 2650 sec elapsed 1 5 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 1.01062 0.06057\n",
      "all done trying2increase usage of unit 853 final usage = 0.98222 2652 sec elapsed 7 43 complete, failed patches, unstarted= 35\n",
      "current avg and SD of usage are 1.01062 0.06052\n",
      "all done trying2increase usage of unit 2389 final usage = 0.91099 2662 sec elapsed 2 24 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01064 0.06046\n",
      "all done trying2increase usage of unit 998 final usage = 0.98897 2667 sec elapsed 10 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01064 0.06041\n",
      "begin usage increase for unit 1472 with usage 0.88806 . Sec, total UU units tried = 2667 220\n",
      "all done trying2increase usage of unit 1472 final usage = 0.91364 2670 sec elapsed 4 12 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.01066 0.06034\n",
      "all done trying2increase usage of unit 2054 final usage = 0.98606 2672 sec elapsed 9 16 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01066 0.06026\n",
      "all done trying2increase usage of unit 2974 final usage = 0.9855 2675 sec elapsed 10 11 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01067 0.06025\n",
      "all done trying2increase usage of unit 613 final usage = 0.99198 2678 sec elapsed 7 0 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.01066 0.06012\n",
      "all done trying2increase usage of unit 1279 final usage = 0.98312 2680 sec elapsed 8 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01068 0.06007\n",
      "all done trying2increase usage of unit 1336 final usage = 0.89055 2681 sec elapsed 0 2 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.01068 0.06007\n",
      "all done trying2increase usage of unit 2390 final usage = 0.89055 2683 sec elapsed 0 2 complete, failed patches, unstarted= 16\n",
      "current avg and SD of usage are 1.01068 0.06007\n",
      "all done trying2increase usage of unit 2381 final usage = 0.89109 2688 sec elapsed 0 6 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.01068 0.06007\n",
      "all done trying2increase usage of unit 2805 final usage = 0.98239 2692 sec elapsed 8 14 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01069 0.05997\n",
      "all done trying2increase usage of unit 2205 final usage = 0.99078 2700 sec elapsed 9 24 complete, failed patches, unstarted= 151\n",
      "current avg and SD of usage are 1.01072 0.05996\n",
      "begin usage increase for unit 1218 with usage 0.89163 . Sec, total UU units tried = 2700 230\n",
      "all done trying2increase usage of unit 1218 final usage = 0.98168 2701 sec elapsed 8 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01074 0.05994\n",
      "all done trying2increase usage of unit 3052 final usage = 0.95259 2706 sec elapsed 3 47 complete, failed patches, unstarted= 46\n",
      "current avg and SD of usage are 1.01073 0.05994\n",
      "all done trying2increase usage of unit 1746 final usage = 0.94466 2726 sec elapsed 5 64 complete, failed patches, unstarted= 21\n",
      "current avg and SD of usage are 1.01075 0.0599\n",
      "all done trying2increase usage of unit 1490 final usage = 0.89263 2731 sec elapsed 0 20 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.01075 0.0599\n",
      "all done trying2increase usage of unit 2239 final usage = 0.99107 2735 sec elapsed 8 32 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01079 0.05984\n",
      "all done trying2increase usage of unit 1866 final usage = 0.92958 2741 sec elapsed 4 97 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.0108 0.05976\n",
      "all done trying2increase usage of unit 3388 final usage = 1.00042 2759 sec elapsed 9 29 complete, failed patches, unstarted= 43\n",
      "current avg and SD of usage are 1.0108 0.05972\n",
      "all done trying2increase usage of unit 2368 final usage = 0.89604 2760 sec elapsed 0 1 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.0108 0.05972\n",
      "all done trying2increase usage of unit 1768 final usage = 0.9984 2763 sec elapsed 7 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01081 0.05967\n",
      "all done trying2increase usage of unit 1517 final usage = 0.91559 2780 sec elapsed 2 58 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01082 0.05963\n",
      "begin usage increase for unit 1482 with usage 0.89731 . Sec, total UU units tried = 2780 240\n",
      "all done trying2increase usage of unit 1482 final usage = 0.98424 2791 sec elapsed 9 32 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.01084 0.05961\n",
      "all done trying2increase usage of unit 70 final usage = 0.98567 2800 sec elapsed 8 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01085 0.05956\n",
      "all done trying2increase usage of unit 3140 final usage = 0.98516 2803 sec elapsed 9 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01085 0.0595\n",
      "all done trying2increase usage of unit 1509 final usage = 0.96114 2827 sec elapsed 8 93 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.01088 0.05946\n",
      "all done trying2increase usage of unit 2444 final usage = 0.98437 2828 sec elapsed 6 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01089 0.05943\n",
      "all done trying2increase usage of unit 1534 final usage = 0.90157 2849 sec elapsed 1 82 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.01089 0.05942\n",
      "all done trying2increase usage of unit 1190 final usage = 0.95727 2865 sec elapsed 5 95 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01093 0.05916\n",
      "all done trying2increase usage of unit 3185 final usage = 0.96507 2882 sec elapsed 7 32 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.01092 0.05914\n",
      "all done trying2increase usage of unit 2851 final usage = 0.90023 2884 sec elapsed 0 25 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.01092 0.05914\n",
      "all done trying2increase usage of unit 142 final usage = 0.90549 2915 sec elapsed 1 117 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.01092 0.05913\n",
      "begin usage increase for unit 84 with usage 0.90058 . Sec, total UU units tried = 2915 250\n",
      "all done trying2increase usage of unit 84 final usage = 0.91068 2961 sec elapsed 1 101 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.01092 0.05912\n",
      "all done trying2increase usage of unit 1175 final usage = 0.98291 2962 sec elapsed 8 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01093 0.0591\n",
      "all done trying2increase usage of unit 1014 final usage = 0.98783 2976 sec elapsed 8 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01094 0.05908\n",
      "all done trying2increase usage of unit 102 final usage = 0.91524 2985 sec elapsed 1 29 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01094 0.05907\n",
      "all done trying2increase usage of unit 1326 final usage = 0.99259 2987 sec elapsed 7 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01093 0.05906\n",
      "all done trying2increase usage of unit 969 final usage = 0.91176 3042 sec elapsed 1 127 complete, failed patches, unstarted= 14\n",
      "current avg and SD of usage are 1.01093 0.05906\n",
      "all done trying2increase usage of unit 2518 final usage = 0.98532 3044 sec elapsed 7 0 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01092 0.05899\n",
      "all done trying2increase usage of unit 2741 final usage = 0.98211 3046 sec elapsed 8 10 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01092 0.05893\n",
      "all done trying2increase usage of unit 870 final usage = 0.9812 3048 sec elapsed 6 5 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.01092 0.05889\n",
      "all done trying2increase usage of unit 2933 final usage = 0.9846 3050 sec elapsed 7 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01093 0.05881\n",
      "begin usage increase for unit 3272 with usage 0.90595 . Sec, total UU units tried = 3050 260\n",
      "all done trying2increase usage of unit 3272 final usage = 0.97955 3102 sec elapsed 7 20 complete, failed patches, unstarted= 110\n",
      "current avg and SD of usage are 1.01096 0.05876\n",
      "all done trying2increase usage of unit 1371 final usage = 0.99672 3105 sec elapsed 7 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01095 0.05876\n",
      "all done trying2increase usage of unit 2387 final usage = 0.90663 3106 sec elapsed 0 2 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.01095 0.05876\n",
      "all done trying2increase usage of unit 2391 final usage = 0.90663 3108 sec elapsed 0 1 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.01095 0.05876\n",
      "all done trying2increase usage of unit 2392 final usage = 0.90663 3109 sec elapsed 0 1 complete, failed patches, unstarted= 25\n",
      "current avg and SD of usage are 1.01095 0.05876\n",
      "all done trying2increase usage of unit 2393 final usage = 0.90663 3112 sec elapsed 0 5 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.01095 0.05876\n",
      "all done trying2increase usage of unit 1747 final usage = 0.93057 3131 sec elapsed 2 27 complete, failed patches, unstarted= 52\n",
      "current avg and SD of usage are 1.01095 0.05872\n",
      "all done trying2increase usage of unit 2250 final usage = 0.98083 3132 sec elapsed 6 12 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01096 0.05869\n",
      "all done trying2increase usage of unit 2614 final usage = 0.98305 3139 sec elapsed 7 45 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01098 0.05864\n",
      "all done trying2increase usage of unit 769 final usage = 0.99596 3141 sec elapsed 7 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.011 0.0586\n",
      "begin usage increase for unit 1529 with usage 0.90881 . Sec, total UU units tried = 3141 270\n",
      "all done trying2increase usage of unit 1529 final usage = 0.91856 3164 sec elapsed 1 85 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.011 0.05859\n",
      "all done trying2increase usage of unit 582 final usage = 0.94634 3167 sec elapsed 4 48 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.01102 0.05844\n",
      "all done trying2increase usage of unit 3047 final usage = 0.98425 3173 sec elapsed 8 24 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01104 0.0584\n",
      "all done trying2increase usage of unit 1196 final usage = 0.98692 3174 sec elapsed 7 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01104 0.05838\n",
      "all done trying2increase usage of unit 963 final usage = 0.99598 3179 sec elapsed 9 61 complete, failed patches, unstarted= 49\n",
      "current avg and SD of usage are 1.01107 0.05836\n",
      "all done trying2increase usage of unit 749 final usage = 0.9212 3182 sec elapsed 1 72 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01107 0.05835\n",
      "all done trying2increase usage of unit 1425 final usage = 0.98161 3186 sec elapsed 5 15 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01109 0.05831\n",
      "all done trying2increase usage of unit 2640 final usage = 0.9254 3201 sec elapsed 2 29 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01109 0.05829\n",
      "all done trying2increase usage of unit 1549 final usage = 0.9515 3205 sec elapsed 6 11 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 1.0111 0.05824\n",
      "all done trying2increase usage of unit 634 final usage = 0.98729 3207 sec elapsed 6 17 complete, failed patches, unstarted= 36\n",
      "current avg and SD of usage are 1.0111 0.05822\n",
      "begin usage increase for unit 2488 with usage 0.91084 . Sec, total UU units tried = 3207 280\n",
      "all done trying2increase usage of unit 2488 final usage = 0.96389 3255 sec elapsed 3 91 complete, failed patches, unstarted= 181\n",
      "current avg and SD of usage are 1.01113 0.05817\n",
      "all done trying2increase usage of unit 2138 final usage = 0.99151 3256 sec elapsed 6 5 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01114 0.05815\n",
      "all done trying2increase usage of unit 2778 final usage = 0.98484 3260 sec elapsed 7 5 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01114 0.05812\n",
      "all done trying2increase usage of unit 2290 final usage = 0.91282 3268 sec elapsed 0 24 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01114 0.05812\n",
      "all done trying2increase usage of unit 1598 final usage = 0.98421 3271 sec elapsed 4 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01114 0.0581\n",
      "all done trying2increase usage of unit 1996 final usage = 0.98338 3274 sec elapsed 6 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01115 0.05809\n",
      "all done trying2increase usage of unit 2454 final usage = 0.93786 3276 sec elapsed 3 76 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.01116 0.05806\n",
      "all done trying2increase usage of unit 1532 final usage = 0.97456 3302 sec elapsed 9 44 complete, failed patches, unstarted= 48\n",
      "current avg and SD of usage are 1.01118 0.05792\n",
      "all done trying2increase usage of unit 2937 final usage = 0.96888 3307 sec elapsed 4 41 complete, failed patches, unstarted= 65\n",
      "current avg and SD of usage are 1.01119 0.05791\n",
      "all done trying2increase usage of unit 412 final usage = 0.99525 3310 sec elapsed 5 15 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.0112 0.05773\n",
      "begin usage increase for unit 1244 with usage 0.91766 . Sec, total UU units tried = 3310 290\n",
      "all done trying2increase usage of unit 1244 final usage = 0.96702 3319 sec elapsed 5 79 complete, failed patches, unstarted= 85\n",
      "current avg and SD of usage are 1.01123 0.05772\n",
      "all done trying2increase usage of unit 1178 final usage = 0.98451 3320 sec elapsed 5 0 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01123 0.0577\n",
      "all done trying2increase usage of unit 200 final usage = 0.93273 3327 sec elapsed 1 18 complete, failed patches, unstarted= 54\n",
      "current avg and SD of usage are 1.01123 0.05769\n",
      "all done trying2increase usage of unit 3274 final usage = 0.98171 3365 sec elapsed 6 31 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.01125 0.05765\n",
      "all done trying2increase usage of unit 2002 final usage = 0.98562 3375 sec elapsed 7 14 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 1.01125 0.05763\n",
      "all done trying2increase usage of unit 2396 final usage = 0.91885 3382 sec elapsed 0 19 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.01125 0.05763\n",
      "all done trying2increase usage of unit 2649 final usage = 0.98422 3383 sec elapsed 6 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01128 0.05759\n",
      "all done trying2increase usage of unit 817 final usage = 0.97522 3385 sec elapsed 5 20 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01129 0.05758\n",
      "all done trying2increase usage of unit 1569 final usage = 0.9877 3395 sec elapsed 7 23 complete, failed patches, unstarted= 15\n",
      "current avg and SD of usage are 1.0113 0.05755\n",
      "all done trying2increase usage of unit 880 final usage = 0.9212 3397 sec elapsed 0 38 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0113 0.05755\n",
      "begin usage increase for unit 108 with usage 0.92185 . Sec, total UU units tried = 3397 300\n",
      "all done trying2increase usage of unit 108 final usage = 0.98376 3411 sec elapsed 7 25 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01131 0.05752\n",
      "all done trying2increase usage of unit 3343 final usage = 0.98349 3414 sec elapsed 6 10 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01131 0.05749\n",
      "all done trying2increase usage of unit 1264 final usage = 0.95039 3423 sec elapsed 2 14 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.01134 0.05748\n",
      "all done trying2increase usage of unit 1066 final usage = 0.98763 3432 sec elapsed 5 34 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01135 0.05747\n",
      "all done trying2increase usage of unit 767 final usage = 0.98517 3433 sec elapsed 7 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01135 0.05744\n",
      "all done trying2increase usage of unit 2710 final usage = 0.98291 3440 sec elapsed 6 37 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01135 0.05745\n",
      "all done trying2increase usage of unit 179 final usage = 0.98599 3442 sec elapsed 6 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01137 0.05742\n",
      "all done trying2increase usage of unit 1219 final usage = 0.98903 3443 sec elapsed 4 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01136 0.05736\n",
      "all done trying2increase usage of unit 2615 final usage = 0.9829 3445 sec elapsed 6 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01137 0.05735\n",
      "all done trying2increase usage of unit 63 final usage = 0.98222 3451 sec elapsed 6 14 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01137 0.05734\n",
      "begin usage increase for unit 602 with usage 0.92577 . Sec, total UU units tried = 3451 310\n",
      "all done trying2increase usage of unit 602 final usage = 0.98192 3454 sec elapsed 7 15 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01137 0.05728\n",
      "all done trying2increase usage of unit 1912 final usage = 0.98429 3456 sec elapsed 5 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01139 0.05716\n",
      "all done trying2increase usage of unit 2978 final usage = 0.98473 3478 sec elapsed 6 19 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.0114 0.05713\n",
      "all done trying2increase usage of unit 2215 final usage = 0.98772 3493 sec elapsed 8 29 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.01141 0.05711\n",
      "all done trying2increase usage of unit 2128 final usage = 0.98146 3504 sec elapsed 4 17 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01141 0.05708\n",
      "all done trying2increase usage of unit 3069 final usage = 0.98914 3507 sec elapsed 6 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01142 0.05706\n",
      "all done trying2increase usage of unit 2333 final usage = 0.92787 3510 sec elapsed 0 5 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.01142 0.05706\n",
      "all done trying2increase usage of unit 2352 final usage = 0.98409 3513 sec elapsed 7 13 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01144 0.05705\n",
      "all done trying2increase usage of unit 1155 final usage = 0.98008 3520 sec elapsed 7 1 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01144 0.05705\n",
      "all done trying2increase usage of unit 2544 final usage = 0.98177 3521 sec elapsed 4 0 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.01144 0.05703\n",
      "begin usage increase for unit 2370 with usage 0.92972 . Sec, total UU units tried = 3521 320\n",
      "all done trying2increase usage of unit 2370 final usage = 0.92972 3523 sec elapsed 0 2 complete, failed patches, unstarted= 50\n",
      "current avg and SD of usage are 1.01144 0.05703\n",
      "all done trying2increase usage of unit 1867 final usage = 0.98734 3533 sec elapsed 7 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01143 0.05701\n",
      "all done trying2increase usage of unit 1530 final usage = 0.95632 3548 sec elapsed 3 46 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.01143 0.057\n",
      "all done trying2increase usage of unit 100 final usage = 0.97588 3554 sec elapsed 5 11 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.01143 0.05698\n",
      "all done trying2increase usage of unit 2962 final usage = 0.98526 3555 sec elapsed 4 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01144 0.05698\n",
      "all done trying2increase usage of unit 275 final usage = 0.99069 3559 sec elapsed 5 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01146 0.05696\n",
      "all done trying2increase usage of unit 85 final usage = 0.99034 3602 sec elapsed 5 73 complete, failed patches, unstarted= 4\n",
      "current avg and SD of usage are 1.01149 0.05692\n",
      "all done trying2increase usage of unit 2746 final usage = 0.98242 3603 sec elapsed 4 9 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01148 0.05677\n",
      "all done trying2increase usage of unit 1400 final usage = 0.98489 3605 sec elapsed 4 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01148 0.05676\n",
      "all done trying2increase usage of unit 2359 final usage = 0.98229 3617 sec elapsed 5 31 complete, failed patches, unstarted= 7\n",
      "current avg and SD of usage are 1.01149 0.05674\n",
      "begin usage increase for unit 2207 with usage 0.93523 . Sec, total UU units tried = 3617 330\n",
      "all done trying2increase usage of unit 2207 final usage = 0.93945 3627 sec elapsed 1 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01149 0.05674\n",
      "all done trying2increase usage of unit 1999 final usage = 0.94494 3648 sec elapsed 1 51 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 1.01149 0.05673\n",
      "all done trying2increase usage of unit 887 final usage = 0.98092 3650 sec elapsed 5 11 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01149 0.05665\n",
      "all done trying2increase usage of unit 1269 final usage = 0.98875 3653 sec elapsed 5 19 complete, failed patches, unstarted= 32\n",
      "current avg and SD of usage are 1.01148 0.05664\n",
      "all done trying2increase usage of unit 2613 final usage = 0.98784 3654 sec elapsed 4 3 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01148 0.05664\n",
      "all done trying2increase usage of unit 2430 final usage = 0.98456 3655 sec elapsed 4 2 complete, failed patches, unstarted= 20\n",
      "current avg and SD of usage are 1.0115 0.05647\n",
      "all done trying2increase usage of unit 3363 final usage = 0.99042 3693 sec elapsed 6 77 complete, failed patches, unstarted= 22\n",
      "current avg and SD of usage are 1.01152 0.05646\n",
      "all done trying2increase usage of unit 3033 final usage = 0.98061 3695 sec elapsed 5 20 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01153 0.05645\n",
      "all done trying2increase usage of unit 610 final usage = 0.98416 3696 sec elapsed 2 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01153 0.05645\n",
      "all done trying2increase usage of unit 2060 final usage = 0.99005 3698 sec elapsed 5 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01152 0.05644\n",
      "begin usage increase for unit 1062 with usage 0.93961 . Sec, total UU units tried = 3698 340\n",
      "all done trying2increase usage of unit 1062 final usage = 0.98266 3702 sec elapsed 5 3 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01153 0.05643\n",
      "all done trying2increase usage of unit 2558 final usage = 0.98298 3707 sec elapsed 4 12 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01155 0.05642\n",
      "all done trying2increase usage of unit 2024 final usage = 0.98368 3711 sec elapsed 4 21 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01156 0.05642\n",
      "all done trying2increase usage of unit 1437 final usage = 0.98242 3716 sec elapsed 3 19 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01157 0.05642\n",
      "all done trying2increase usage of unit 2350 final usage = 0.94878 3762 sec elapsed 1 68 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.01157 0.05642\n",
      "all done trying2increase usage of unit 552 final usage = 0.97011 3769 sec elapsed 2 128 complete, failed patches, unstarted= 115\n",
      "current avg and SD of usage are 1.01156 0.05639\n",
      "all done trying2increase usage of unit 2007 final usage = 0.95029 3808 sec elapsed 1 121 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01157 0.05639\n",
      "all done trying2increase usage of unit 416 final usage = 0.98349 3813 sec elapsed 4 25 complete, failed patches, unstarted= 30\n",
      "current avg and SD of usage are 1.01157 0.05635\n",
      "all done trying2increase usage of unit 2749 final usage = 0.9876 3814 sec elapsed 4 10 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01157 0.05623\n",
      "all done trying2increase usage of unit 622 final usage = 0.98536 3815 sec elapsed 5 4 complete, failed patches, unstarted= 23\n",
      "current avg and SD of usage are 1.01159 0.05622\n",
      "begin usage increase for unit 2774 with usage 0.94442 . Sec, total UU units tried = 3815 350\n",
      "all done trying2increase usage of unit 2774 final usage = 0.98372 3821 sec elapsed 4 33 complete, failed patches, unstarted= 26\n",
      "current avg and SD of usage are 1.01161 0.0562\n",
      "all done trying2increase usage of unit 869 final usage = 0.98945 3822 sec elapsed 4 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01161 0.0562\n",
      "all done trying2increase usage of unit 99 final usage = 0.98267 3834 sec elapsed 4 23 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.01161 0.05618\n",
      "all done trying2increase usage of unit 710 final usage = 0.98213 3835 sec elapsed 3 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0116 0.05618\n",
      "all done trying2increase usage of unit 25 final usage = 0.98941 3843 sec elapsed 3 19 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01161 0.05614\n",
      "all done trying2increase usage of unit 1141 final usage = 0.99106 3845 sec elapsed 3 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01161 0.05614\n",
      "all done trying2increase usage of unit 3172 final usage = 0.98771 3846 sec elapsed 3 5 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01162 0.05612\n",
      "all done trying2increase usage of unit 2517 final usage = 0.98317 3849 sec elapsed 3 1 complete, failed patches, unstarted= 6\n",
      "current avg and SD of usage are 1.01162 0.05612\n",
      "all done trying2increase usage of unit 1153 final usage = 0.98718 3853 sec elapsed 5 1 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01161 0.05611\n",
      "all done trying2increase usage of unit 2303 final usage = 0.96483 3859 sec elapsed 2 19 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01162 0.0561\n",
      "begin usage increase for unit 2401 with usage 0.94759 . Sec, total UU units tried = 3859 360\n",
      "all done trying2increase usage of unit 2401 final usage = 0.97696 3861 sec elapsed 4 2 complete, failed patches, unstarted= 37\n",
      "current avg and SD of usage are 1.01162 0.0561\n",
      "all done trying2increase usage of unit 2385 final usage = 0.98425 3870 sec elapsed 4 18 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01165 0.05606\n",
      "all done trying2increase usage of unit 54 final usage = 0.98398 3881 sec elapsed 4 27 complete, failed patches, unstarted= 18\n",
      "current avg and SD of usage are 1.01166 0.05605\n",
      "all done trying2increase usage of unit 3279 final usage = 0.95494 3906 sec elapsed 1 41 complete, failed patches, unstarted= 34\n",
      "current avg and SD of usage are 1.01166 0.05604\n",
      "all done trying2increase usage of unit 2657 final usage = 0.98135 3907 sec elapsed 3 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01167 0.05604\n",
      "all done trying2increase usage of unit 1838 final usage = 0.96173 3909 sec elapsed 1 36 complete, failed patches, unstarted= 68\n",
      "current avg and SD of usage are 1.01168 0.05603\n",
      "all done trying2increase usage of unit 2156 final usage = 0.98331 3911 sec elapsed 2 16 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01167 0.05601\n",
      "all done trying2increase usage of unit 782 final usage = 0.98468 3939 sec elapsed 3 54 complete, failed patches, unstarted= 8\n",
      "current avg and SD of usage are 1.01169 0.05599\n",
      "all done trying2increase usage of unit 3264 final usage = 1.00568 3973 sec elapsed 4 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01171 0.05598\n",
      "all done trying2increase usage of unit 832 final usage = 0.99343 3974 sec elapsed 3 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0117 0.05597\n",
      "begin usage increase for unit 2887 with usage 0.95191 . Sec, total UU units tried = 3975 370\n",
      "all done trying2increase usage of unit 2887 final usage = 0.98182 3975 sec elapsed 2 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01171 0.05593\n",
      "all done trying2increase usage of unit 139 final usage = 0.98518 3976 sec elapsed 3 4 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0117 0.05593\n",
      "all done trying2increase usage of unit 757 final usage = 0.98197 3978 sec elapsed 4 2 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01171 0.05585\n",
      "all done trying2increase usage of unit 3131 final usage = 0.9634 3980 sec elapsed 1 65 complete, failed patches, unstarted= 72\n",
      "current avg and SD of usage are 1.01172 0.05585\n",
      "all done trying2increase usage of unit 3376 final usage = 0.98174 3996 sec elapsed 3 43 complete, failed patches, unstarted= 9\n",
      "current avg and SD of usage are 1.01172 0.05584\n",
      "all done trying2increase usage of unit 2995 final usage = 0.98914 3997 sec elapsed 4 0 complete, failed patches, unstarted= 3\n",
      "current avg and SD of usage are 1.01173 0.05582\n",
      "all done trying2increase usage of unit 2994 final usage = 0.95332 4000 sec elapsed 0 76 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01173 0.05582\n",
      "all done trying2increase usage of unit 2241 final usage = 0.98029 4002 sec elapsed 2 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01173 0.05581\n",
      "all done trying2increase usage of unit 2571 final usage = 0.98991 4007 sec elapsed 3 8 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01174 0.05581\n",
      "all done trying2increase usage of unit 2705 final usage = 0.98257 4009 sec elapsed 2 7 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01176 0.05579\n",
      "begin usage increase for unit 3198 with usage 0.95408 . Sec, total UU units tried = 4009 380\n",
      "all done trying2increase usage of unit 3198 final usage = 0.98444 4036 sec elapsed 4 14 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01176 0.05578\n",
      "all done trying2increase usage of unit 1129 final usage = 0.9804 4040 sec elapsed 3 0 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01177 0.05577\n",
      "all done trying2increase usage of unit 2408 final usage = 0.96004 4069 sec elapsed 1 53 complete, failed patches, unstarted= 5\n",
      "current avg and SD of usage are 1.01177 0.05577\n",
      "all done trying2increase usage of unit 215 final usage = 0.98197 4071 sec elapsed 2 0 complete, failed patches, unstarted= 54\n",
      "current avg and SD of usage are 1.01176 0.05577\n",
      "all done trying2increase usage of unit 2944 final usage = 0.9821 4072 sec elapsed 3 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01178 0.05576\n",
      "all done trying2increase usage of unit 3070 final usage = 0.98789 4073 sec elapsed 3 6 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01178 0.05575\n",
      "all done trying2increase usage of unit 1471 final usage = 0.95475 4076 sec elapsed 0 7 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 1.01178 0.05575\n",
      "all done trying2increase usage of unit 2331 final usage = 0.95601 4086 sec elapsed 0 10 complete, failed patches, unstarted= 28\n",
      "current avg and SD of usage are 1.01178 0.05575\n",
      "all done trying2increase usage of unit 800 final usage = 0.98276 4088 sec elapsed 2 10 complete, failed patches, unstarted= 86\n",
      "current avg and SD of usage are 1.01179 0.05573\n",
      "all done trying2increase usage of unit 2786 final usage = 0.98079 4089 sec elapsed 2 2 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01179 0.05573\n",
      "begin usage increase for unit 1753 with usage 0.95657 . Sec, total UU units tried = 4089 390\n",
      "all done trying2increase usage of unit 1753 final usage = 0.98648 4089 sec elapsed 2 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01179 0.05571\n",
      "all done trying2increase usage of unit 426 final usage = 0.9839 4095 sec elapsed 3 23 complete, failed patches, unstarted= 92\n",
      "current avg and SD of usage are 1.01179 0.05568\n",
      "all done trying2increase usage of unit 1144 final usage = 0.98224 4098 sec elapsed 2 1 complete, failed patches, unstarted= 1\n",
      "current avg and SD of usage are 1.01179 0.05568\n",
      "all done trying2increase usage of unit 1295 final usage = 0.98518 4099 sec elapsed 3 2 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0118 0.05568\n",
      "all done trying2increase usage of unit 1939 final usage = 0.98356 4115 sec elapsed 3 21 complete, failed patches, unstarted= 12\n",
      "current avg and SD of usage are 1.01181 0.05568\n",
      "all done trying2increase usage of unit 3153 final usage = 0.95737 4121 sec elapsed 0 18 complete, failed patches, unstarted= 40\n",
      "current avg and SD of usage are 1.01181 0.05568\n",
      "all done trying2increase usage of unit 1871 final usage = 0.95762 4167 sec elapsed 0 105 complete, failed patches, unstarted= 17\n",
      "current avg and SD of usage are 1.01181 0.05568\n",
      "all done trying2increase usage of unit 918 final usage = 0.98378 4175 sec elapsed 3 29 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.01181 0.05566\n",
      "all done trying2increase usage of unit 28 final usage = 0.98609 4176 sec elapsed 2 0 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.0118 0.05566\n",
      "all done trying2increase usage of unit 1450 final usage = 0.98037 4182 sec elapsed 3 21 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 1.0118 0.05567\n",
      "begin usage increase for unit 2856 with usage 0.95819 . Sec, total UU units tried = 4182 400\n",
      "all done trying2increase usage of unit 2856 final usage = 0.98745 4186 sec elapsed 3 8 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 1.01181 0.05566\n"
     ]
    }
   ],
   "source": [
    "print(\"much better, but let's do one more underpatch pass\")\n",
    "maxSD = 0.055  #0.07  #0.055   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = 0.98 #float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "maxExchangePop = 0.25*aDP #this is widened vs. vanillaHD to overcome blockiness\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "debug1 = 10 #int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop),\"= aDP*\",r3(maxExchangePop/aDP) ) \n",
    "maxGap = aDP - minDistrictPop #0.9 * np.median(unitPop)   #aDP - minDistrictPop  \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list(  set(popHDlist).difference( set(hasSplitUnit) )  )\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries:\n",
    "    #simplified vs. vanillaHD - always work on the lowest-used unit.\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedSmallUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = 999.  #preventing re-selection\n",
    "    UUU = eligUnitUse.index(np.min(eligUnitUse))\n",
    "    attemptedSmallUs.append(UUU)\n",
    "    UUUc = [UUU] + unitNbrs[UUU]\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists, UUUcSet = list(), list(), set(UUUc)\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUcSet) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 40 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(UUUcSet)\n",
    "        tryPop = np.sum([unitPop[u] for u in trySet])\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig and tryPop < aDP + maxExchangePop): \n",
    "            #we can't add whole cluster; neighbors may be enclavy or too much pop.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates + [homeU[t]]:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            currPop = np.sum([ unitPop[u] for u in trySet] )\n",
    "            if abs(currPop - aDP) <= maxGap: \n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying2increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"complete, failed patches, unstarted=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b569fbb3-aa01-414e-b9fc-a8678d3a44c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KxxU1Z/Px8ZGPj48rpwEAAOool5+BqQgvBw4c0CeffKKmTZs6jUdFRSkvL08ZGRn2uo0bN6q8vFy9evWya7Zs2aLS0lK7Ji0tTe3atVOTJk1c3TIAADBMpQNMYWGhMjMzlZmZKUk6dOiQMjMzdfjwYZWWluq///u/9cUXXyglJUVlZWVyOBxyOBwqKSmRJHXo0EEDBw7UQw89pB07duhf//qXEhISNHLkSEVEREiSRo0aJW9vb40bN0579+7V6tWrtWjRIo0fP951MwcAAMaq9FtIX3zxhfr162c/rggVY8aM0YwZM/TRRx9Jkrp16+a03aeffqq+fftKklJSUpSQkKD+/fvL3d1dw4cP1+LFi+3awMBArV+/XvHx8erevbuaNWum6dOn8xFqAAAgqQoBpm/fvrIs64LjFxurEBwcrFWrVl20pkuXLvrss88q2x4AAGgA+C4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA41fJljgCA2nU075Ryi0ouOP5tTmENdgO4HgEGAOqZo3mnFP3iZp0qLbtonZ+Xh5r4e9dQV4BrEWAAoJ7JLSrRqdIyLRzRTdeEXHHBuib+3moR5FeDnQGuQ4ABgHrqmpArdF2LwNpuA6gWXMQLAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjVDrAbNmyRUOHDlVERITc3Ny0Zs0ap3HLsjR9+nSFh4fLz89P0dHROnDggFPNiRMnFBsbq4CAAAUFBWncuHEqLCx0qvnqq6906623ytfXV5GRkZo3b17lZwcAAOqlSgeYoqIide3aVcnJyecdnzdvnhYvXqylS5dq+/bt8vf3V0xMjE6fPm3XxMbGau/evUpLS9PatWu1ZcsWxcXF2eMFBQUaMGCAWrVqpYyMDL3wwguaMWOGXnvttSpMEQAA1Deeld1g0KBBGjRo0HnHLMvSwoULNXXqVA0bNkyS9Oabbyo0NFRr1qzRyJEjtX//fqWmpmrnzp3q0aOHJCkpKUmDBw/W/PnzFRERoZSUFJWUlOiNN96Qt7e3OnXqpMzMTC1YsMAp6AAAgIbJpdfAHDp0SA6HQ9HR0fa6wMBA9erVS+np6ZKk9PR0BQUF2eFFkqKjo+Xu7q7t27fbNbfddpu8vb3tmpiYGGVlZSk3N/e8xy4uLlZBQYHTAgAA6ieXBhiHwyFJCg0NdVofGhpqjzkcDoWEhDiNe3p6Kjg42KnmfPv49THONnfuXAUGBtpLZGTk5U8IAADUSfXmU0iTJ09Wfn6+vRw5cqS2WwIAANXEpQEmLCxMkpSdne20Pjs72x4LCwtTTk6O0/iZM2d04sQJp5rz7ePXxzibj4+PAgICnBYAAFA/uTTAtG7dWmFhYdqwYYO9rqCgQNu3b1dUVJQkKSoqSnl5ecrIyLBrNm7cqPLycvXq1cuu2bJli0pLS+2atLQ0tWvXTk2aNHFlywAAwECVDjCFhYXKzMxUZmampF8u3M3MzNThw4fl5uamxMREzZkzRx999JH27Nmj++67TxEREbrrrrskSR06dNDAgQP10EMPaceOHfrXv/6lhIQEjRw5UhEREZKkUaNGydvbW+PGjdPevXu1evVqLVq0SOPHj3fZxAEAgLkq/THqL774Qv369bMfV4SKMWPGaMWKFXrqqadUVFSkuLg45eXlqXfv3kpNTZWvr6+9TUpKihISEtS/f3+5u7tr+PDhWrx4sT0eGBio9evXKz4+Xt27d1ezZs00ffp0PkINAAAkVSHA9O3bV5ZlXXDczc1Ns2bN0qxZsy5YExwcrFWrVl30OF26dNFnn31W2fYAAEADUG8+hQQAABoOAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA47g8wJSVlWnatGlq3bq1/Pz81KZNG82ePVuWZdk1lmVp+vTpCg8Pl5+fn6Kjo3XgwAGn/Zw4cUKxsbEKCAhQUFCQxo0bp8LCQle3CwAADOTyAPP8889ryZIlevnll7V//349//zzmjdvnpKSkuyaefPmafHixVq6dKm2b98uf39/xcTE6PTp03ZNbGys9u7dq7S0NK1du1ZbtmxRXFycq9sFAAAG8nT1Drdu3aphw4ZpyJAhkqSrrrpKb731lnbs2CHpl7MvCxcu1NSpUzVs2DBJ0ptvvqnQ0FCtWbNGI0eO1P79+5WamqqdO3eqR48ekqSkpCQNHjxY8+fPV0REhKvbBgAABnH5GZibb75ZGzZs0DfffCNJ+vLLL/X5559r0KBBkqRDhw7J4XAoOjra3iYwMFC9evVSenq6JCk9PV1BQUF2eJGk6Ohoubu7a/v27a5uGQAAGMblZ2AmTZqkgoICtW/fXh4eHiorK9Mzzzyj2NhYSZLD4ZAkhYaGOm0XGhpqjzkcDoWEhDg36ump4OBgu+ZsxcXFKi4uth8XFBS4bE4AAKBucfkZmL///e9KSUnRqlWrtGvXLq1cuVLz58/XypUrXX0oJ3PnzlVgYKC9REZGVuvxAABA7XF5gJkwYYImTZqkkSNHqnPnzho9erSeeOIJzZ07V5IUFhYmScrOznbaLjs72x4LCwtTTk6O0/iZM2d04sQJu+ZskydPVn5+vr0cOXLE1VMDAAB1hMsDzMmTJ+Xu7rxbDw8PlZeXS5Jat26tsLAwbdiwwR4vKCjQ9u3bFRUVJUmKiopSXl6eMjIy7JqNGzeqvLxcvXr1Ou9xfXx8FBAQ4LQAAID6yeXXwAwdOlTPPPOMWrZsqU6dOmn37t1asGCBHnjgAUmSm5ubEhMTNWfOHLVt21atW7fWtGnTFBERobvuukuS1KFDBw0cOFAPPfSQli5dqtLSUiUkJGjkyJF8AgkAALg+wCQlJWnatGn605/+pJycHEVEROjhhx/W9OnT7ZqnnnpKRUVFiouLU15ennr37q3U1FT5+vraNSkpKUpISFD//v3l7u6u4cOHa/Hixa5uFwAAGMjN+vUtcuuRgoICBQYGKj8/n7eTADQoXx/N151Jn2vto711XYvAWt8PUBmX+vrNdyEBAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjFMtAebo0aP6wx/+oKZNm8rPz0+dO3fWF198YY9blqXp06crPDxcfn5+io6O1oEDB5z2ceLECcXGxiogIEBBQUEaN26cCgsLq6NdAABgGJcHmNzcXN1yyy3y8vLSP//5T+3bt08vvviimjRpYtfMmzdPixcv1tKlS7V9+3b5+/srJiZGp0+ftmtiY2O1d+9epaWlae3atdqyZYvi4uJc3S4AADCQp6t3+PzzzysyMlLLly+317Vu3dr+t2VZWrhwoaZOnaphw4ZJkt58802FhoZqzZo1GjlypPbv36/U1FTt3LlTPXr0kCQlJSVp8ODBmj9/viIiIlzdNgAAMIjLz8B89NFH6tGjh37/+98rJCRE119/vV5//XV7/NChQ3I4HIqOjrbXBQYGqlevXkpPT5ckpaenKygoyA4vkhQdHS13d3dt3779vMctLi5WQUGB0wIAAOonlweYgwcPasmSJWrbtq0+/vhjPfLII3rssce0cuVKSZLD4ZAkhYaGOm0XGhpqjzkcDoWEhDiNe3p6Kjg42K4529y5cxUYGGgvkZGRrp4aAACoI1weYMrLy3XDDTfo2Wef1fXXX6+4uDg99NBDWrp0qasP5WTy5MnKz8+3lyNHjlTr8QAAQO1xeYAJDw9Xx44dndZ16NBBhw8fliSFhYVJkrKzs51qsrOz7bGwsDDl5OQ4jZ85c0YnTpywa87m4+OjgIAApwUAANRPLg8wt9xyi7KyspzWffPNN2rVqpWkXy7oDQsL04YNG+zxgoICbd++XVFRUZKkqKgo5eXlKSMjw67ZuHGjysvL1atXL1e3DAAADOPyTyE98cQTuvnmm/Xss8/qnnvu0Y4dO/Taa6/ptddekyS5ubkpMTFRc+bMUdu2bdW6dWtNmzZNERERuuuuuyT9csZm4MCB9ltPpaWlSkhI0MiRI/kEEgAAcH2AufHGG/XBBx9o8uTJmjVrllq3bq2FCxcqNjbWrnnqqadUVFSkuLg45eXlqXfv3kpNTZWvr69dk5KSooSEBPXv31/u7u4aPny4Fi9e7Op2AQCAgVweYCTpzjvv1J133nnBcTc3N82aNUuzZs26YE1wcLBWrVpVHe0BAADD8V1IAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAONUe4B57rnn5ObmpsTERHvd6dOnFR8fr6ZNm+qKK67Q8OHDlZ2d7bTd4cOHNWTIEDVq1EghISGaMGGCzpw5U93tAgAAA1RrgNm5c6deffVVdenSxWn9E088oX/84x965513tHnzZh07dkx33323PV5WVqYhQ4aopKREW7du1cqVK7VixQpNnz69OtsFAACGqLYAU1hYqNjYWL3++utq0qSJvT4/P1/Lli3TggULdPvtt6t79+5avny5tm7dqm3btkmS1q9fr3379ulvf/ubunXrpkGDBmn27NlKTk5WSUlJdbUMAAAMUW0BJj4+XkOGDFF0dLTT+oyMDJWWljqtb9++vVq2bKn09HRJUnp6ujp37qzQ0FC7JiYmRgUFBdq7d+95j1dcXKyCggKnBQAA1E+e1bHTt99+W7t27dLOnTvPGXM4HPL29lZQUJDT+tDQUDkcDrvm1+GlYrxi7Hzmzp2rmTNnuqB7AABQ17n8DMyRI0f0+OOPKyUlRb6+vq7e/QVNnjxZ+fn59nLkyJEaOzYAAKhZLg8wGRkZysnJ0Q033CBPT095enpq8+bNWrx4sTw9PRUaGqqSkhLl5eU5bZedna2wsDBJUlhY2DmfSqp4XFFzNh8fHwUEBDgtAACgfnJ5gOnfv7/27NmjzMxMe+nRo4diY2Ptf3t5eWnDhg32NllZWTp8+LCioqIkSVFRUdqzZ49ycnLsmrS0NAUEBKhjx46ubhkAABjG5dfANG7cWNddd53TOn9/fzVt2tReP27cOI0fP17BwcEKCAjQo48+qqioKN10002SpAEDBqhjx44aPXq05s2bJ4fDoalTpyo+Pl4+Pj6ubhkAABimWi7i/S0vvfSS3N3dNXz4cBUXFysmJkavvPKKPe7h4aG1a9fqkUceUVRUlPz9/TVmzBjNmjWrNtoFAAB1TI0EmE2bNjk99vX1VXJyspKTky+4TatWrbRu3bpq7gwAAJiI70ICAADGqZW3kAAA5vg2p/Ci4038vdUiyK+GugF+QYABAJxXE39v+Xl5KHF15kXr/Lw89MmTfQgxqFEEGADAebUI8tMnT/ZRbtGFv4Pu25xCJa7OVG5RCQEGNYoAAwC4oBZBfgQT1ElcxAsAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjOPyADN37lzdeOONaty4sUJCQnTXXXcpKyvLqeb06dOKj49X06ZNdcUVV2j48OHKzs52qjl8+LCGDBmiRo0aKSQkRBMmTNCZM2dc3S4AADCQywPM5s2bFR8fr23btiktLU2lpaUaMGCAioqK7JonnnhC//jHP/TOO+9o8+bNOnbsmO6++257vKysTEOGDFFJSYm2bt2qlStXasWKFZo+fbqr2wUAAAbydPUOU1NTnR6vWLFCISEhysjI0G233ab8/HwtW7ZMq1at0u233y5JWr58uTp06KBt27bppptu0vr167Vv3z598sknCg0NVbdu3TR79mxNnDhRM2bMkLe3t6vbBgAABqn2a2Dy8/MlScHBwZKkjIwMlZaWKjo62q5p3769WrZsqfT0dElSenq6OnfurNDQULsmJiZGBQUF2rt3b3W3DAAA6jiXn4H5tfLyciUmJuqWW27RddddJ0lyOBzy9vZWUFCQU21oaKgcDodd8+vwUjFeMXY+xcXFKi4uth8XFBS4ahoAAKCOqdYzMPHx8fr666/19ttvV+dhJP1y8XBgYKC9REZGVvsxAQBA7ai2AJOQkKC1a9fq008/1ZVXXmmvDwsLU0lJifLy8pzqs7OzFRYWZtec/amkiscVNWebPHmy8vPz7eXIkSMunA0AAKhLXB5gLMtSQkKCPvjgA23cuFGtW7d2Gu/evbu8vLy0YcMGe11WVpYOHz6sqKgoSVJUVJT27NmjnJwcuyYtLU0BAQHq2LHjeY/r4+OjgIAApwUAANRPLr8GJj4+XqtWrdKHH36oxo0b29esBAYGys/PT4GBgRo3bpzGjx+v4OBgBQQE6NFHH1VUVJRuuukmSdKAAQPUsWNHjR49WvPmzZPD4dDUqVMVHx8vHx8fV7cMAAAM4/IAs2TJEklS3759ndYvX75c999/vyTppZdekru7u4YPH67i4mLFxMTolVdesWs9PDy0du1aPfLII4qKipK/v7/GjBmjWbNmubpdAABgIJcHGMuyfrPG19dXycnJSk5OvmBNq1attG7dOle2BgAA6gm+CwkAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjOPyrxIAAFSvo3mnlFtUcsHxb3MKa7AboHYQYADAIEfzTin6xc06VVp20To/Lw818feuoa6AmkeAAQCD5BaV6FRpmRaO6KZrQq64YF0Tf2+1CPKrwc6AmkWAAQADXRNyha5rEVjbbQC1hot4AQCAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjcCM7AMBl+63vX+LOwHA1AgwAoMqa+HvLz8tDiaszL1rn5+WhT57sQ4iByxBgAABV1iLIT5882ec3vx07cXWmcotKCDBwGQIMAOCytAjyI5igxnERLwAAMA4BBgAAGIcAAwAAjEOAAQAAxuEiXgCoQ47mnfrNT/QAIMAAQJ1xNO+Uol/crFOlZRet8/PyUBN/7xrqCqibCDAAUEfkFpXoVGmZFo7opmtCrrhgnal3teVuvXAlAgwA1DHXhFyh61oE1nYbLsPdelEdCDAAgGrF3XpRHQgwAIBqx9164Wp8jBoAABiHMzAAUEP4iPRv40JfXCoCDAC4wG+Fk5+KSvTHv2bwEekL4EJfVFadDjDJycl64YUX5HA41LVrVyUlJalnz5613RaAavZbYaCuqUw4WflATzW9SEBpqGcYuNAXlVVnA8zq1as1fvx4LV26VL169dLChQsVExOjrKwshYSE1HZ7AM7DFcHjUsNAXUM4uXyXeqGvK95q47kwn5tlWVZtN3E+vXr10o033qiXX35ZklReXq7IyEg9+uijmjRp0m9uX1BQoMDAQOXn5ysgIKC6221QLuVFqib/ONS1fhqqS72L7KXw8/LQ0tHdLxoG6hp+x6pfXfwd43l3vUt9/a6TZ2BKSkqUkZGhyZMn2+vc3d0VHR2t9PT0825TXFys4uJi+3F+fr6kX34QrvZjwWn9WFj824X10ImTpUp8e7dOl5ZftM7Xy10LR16v4EZeDaqfhuzgj0UqKvxZz93dWVc397+sfQU18lZEkI+LOqsppSooKK3tJuq1xu7SBw9dr7yTl3eWr+Lvxuglmy67p4b8t6X5FT5qHuDr8v1WvG7/5vkVqw46evSoJcnaunWr0/oJEyZYPXv2PO82Tz/9tCWJhYWFhYWFpR4sR44cuWhWqJNnYKpi8uTJGj9+vP24vLxcJ06cUNOmTeXm5lZrfRUUFCgyMlJHjhyp929lNaS5Sg1rvg1prlLDmm9DmqvUsOZr6lwty9LPP/+siIiIi9bVyQDTrFkzeXh4KDs722l9dna2wsLCzruNj4+PfHycTzkHBQVVV4uVFhAQYNQv0OVoSHOVGtZ8G9JcpYY134Y0V6lhzdfEuQYGBv5mTZ28E6+3t7e6d++uDRs22OvKy8u1YcMGRUVF1WJnAACgLqiTZ2Akafz48RozZox69Oihnj17auHChSoqKtLYsWNruzUAAFDL6myAGTFihH788UdNnz5dDodD3bp1U2pqqkJDQ2u7tUrx8fHR008/fc7bW/VRQ5qr1LDm25DmKjWs+TakuUoNa771fa519j4wAAAAF1Inr4EBAAC4GAIMAAAwDgEGAAAYhwADAACMQ4BxgeTkZF111VXy9fVVr169tGPHjgvWrlixQm5ubk6Lr6/rv0uiulRmrpKUl5en+Ph4hYeHy8fHR9dee63WrVtXQ91evsrMt2/fvuc8t25ubhoyZEgNdlx1lX1uFy5cqHbt2snPz0+RkZF64okndPr06Rrq9vJVZr6lpaWaNWuW2rRpI19fX3Xt2lWpqak12G3VbdmyRUOHDlVERITc3Ny0Zs2a39xm06ZNuuGGG+Tj46NrrrlGK1asqPY+XaGycz1+/LhGjRqla6+9Vu7u7kpMTKyRPl2lsvN9//33dccdd6h58+YKCAhQVFSUPv7445ppthoQYC7T6tWrNX78eD399NPatWuXunbtqpiYGOXk5Fxwm4CAAB0/ftxefvjhhxrsuOoqO9eSkhLdcccd+v777/Xuu+8qKytLr7/+ulq0aFHDnVdNZef7/vvvOz2vX3/9tTw8PPT73/++hjuvvMrOddWqVZo0aZKefvpp7d+/X8uWLdPq1av15z//uYY7r5rKznfq1Kl69dVXlZSUpH379umPf/yj/uu//ku7d++u4c4rr6ioSF27dlVycvIl1R86dEhDhgxRv379lJmZqcTERD344INGvNBVdq7FxcVq3ry5pk6dqq5du1Zzd65X2flu2bJFd9xxh9atW6eMjAz169dPQ4cONeL3+Lxc8/WLDVfPnj2t+Ph4+3FZWZkVERFhzZ0797z1y5cvtwIDA2uoO9eq7FyXLFliXX311VZJSUlNtehSlZ3v2V566SWrcePGVmFhYXW16DKVnWt8fLx1++23O60bP368dcstt1Rrn65S2fmGh4dbL7/8stO6u+++24qNja3WPl1NkvXBBx9ctOapp56yOnXq5LRuxIgRVkxMTDV25nqXMtdf69Onj/X4449XWz/VrbLzrdCxY0dr5syZrm+oBnAG5jKUlJQoIyND0dHR9jp3d3dFR0crPT39gtsVFhaqVatWioyM1LBhw7R3796aaPeyVGWuH330kaKiohQfH6/Q0FBdd911evbZZ1VWVlZTbVdZVZ/bX1u2bJlGjhwpf3//6mrTJaoy15tvvlkZGRn22y4HDx7UunXrNHjw4Brp+XJUZb7FxcXnvNXr5+enzz//vFp7rQ3p6elOPxtJiomJueTfe5ijvLxcP//8s4KDg2u7lSohwFyG//u//1NZWdk5dwcODQ2Vw+E47zbt2rXTG2+8oQ8//FB/+9vfVF5erptvvln/+c9/aqLlKqvKXA8ePKh3331XZWVlWrdunaZNm6YXX3xRc+bMqYmWL0tV5vtrO3bs0Ndff60HH3ywulp0marMddSoUZo1a5Z69+4tLy8vtWnTRn379jXiLaSqzDcmJkYLFizQgQMHVF5errS0NPstw/rG4XCc92dTUFCgU6dO1VJXqA7z589XYWGh7rnnntpupUoIMDUsKipK9913n7p166Y+ffro/fffV/PmzfXqq6/WdmsuV15erpCQEL322mvq3r27RowYoSlTpmjp0qW13Vq1W7ZsmTp37qyePXvWdivVYtOmTXr22Wf1yiuvaNeuXXr//ff1v//7v5o9e3Ztt1YtFi1apLZt26p9+/by9vZWQkKCxo4dK3d3/oTCTKtWrdLMmTP197//XSEhIbXdTpXU2e9CMkGzZs3k4eGh7Oxsp/XZ2dkKCwu7pH14eXnp+uuv17ffflsdLbpMVeYaHh4uLy8veXh42Os6dOggh8OhkpISeXt7V2vPl+NyntuioiK9/fbbmjVrVnW26DJVmeu0adM0evRo+wxT586dVVRUpLi4OE2ZMqVOv7BXZb7NmzfXmjVrdPr0af3000+KiIjQpEmTdPXVV9dEyzUqLCzsvD+bgIAA+fn51VJXcKW3335bDz74oN55551z3i40Sd39K2MAb29vde/eXRs2bLDXlZeXa8OGDYqKirqkfZSVlWnPnj0KDw+vrjZdoipzveWWW/Ttt9+qvLzcXvfNN98oPDy8TocX6fKe23feeUfFxcX6wx/+UN1tukRV5nry5MlzQkpFULXq+NerXc5z6+vrqxYtWujMmTN67733NGzYsOput8ZFRUU5/WwkKS0t7ZL/pqFue+uttzR27Fi99dZbxtzi4YJq+ypi07399tuWj4+PtWLFCmvfvn1WXFycFRQUZDkcDsuyLGv06NHWpEmT7PqZM2daH3/8sfXdd99ZGRkZ1siRIy1fX19r7969tTWFS1bZuR4+fNhq3LixlZCQYGVlZVlr1661QkJCrDlz5tTWFCqlsvOt0Lt3b2vEiBE13e5lqexcn376aatx48bWW2+9ZR08eNBav3691aZNG+uee+6prSlUSmXnu23bNuu9996zvvvuO2vLli3W7bffbrVu3drKzc2tpRlcup9//tnavXu3tXv3bkuStWDBAmv37t3WDz/8YFmWZU2aNMkaPXq0XX/w4EGrUaNG1oQJE6z9+/dbycnJloeHh5WamlpbU7hklZ2rZVl2fffu3a1Ro0ZZu3fvNuLvsWVVfr4pKSmWp6enlZycbB0/ftxe8vLyamsKl4UA4wJJSUlWy5YtLW9vb6tnz57Wtm3b7LE+ffpYY8aMsR8nJibataGhodbgwYOtXbt21ULXVVOZuVqWZW3dutXq1auX5ePjY1199dXWM888Y505c6aGu666ys733//+tyXJWr9+fQ13evkqM9fS0lJrxowZVps2bSxfX18rMjLS+tOf/mTEC3qFysx306ZNVocOHSwfHx+radOm1ujRo62jR4/WQteV9+mnn1qSzlkq5jdmzBirT58+52zTrVs3y9vb27r66qut5cuX13jfVVGVuZ6vvlWrVjXee1VUdr59+vS5aL1p3Cyrjp/vBQAAOAvXwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgnP8HyXD6lX01IFgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 1.01181 and SD 0.05566\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n",
      "unitUse by unitPop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if t in hasSplitUnit:\n",
    "        patchedUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "\n",
    "re_overPatchedUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "plt.scatter(unitPop,patchedUse,label=\"patched\")\n",
    "#plt.scatter(unitPop,unpatchedUnitUse,label=\"unpatched\")\n",
    "print(\"unitUse by unitPop\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac18169-8cf1-408b-b328-035f1b45b30e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "cf287ba6-b50f-4ad3-890f-41b3c7be1f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, all HDs are contiguous and within pop tolerances\n",
      "Now, we need to assign blockgroups included in the HD for the 0 HDs with a split unit\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, all HDs are contiguous and within pop tolerances\")\n",
    "print(\"Now, we need to assign blockgroups included in the HD for the\",len(hasSplitUnit),\"HDs with a split unit\")\n",
    "#tractCP = [tractGeom[b].centroid for b in range(nTracts)]  #should already be defined\n",
    "#      BELOW IS SIMILAR TO OH_muniSnap99, but using block group nomenclature\n",
    "\n",
    "splitBGlist = [list() for t in range(nHDs)] #each HD's final list of blockgroups from a split unit\n",
    "for i,t in enumerate(hasSplitUnit):\n",
    "    fullPop = np.sum([unitPop[u] for u in HDunitList[t] ])\n",
    "    fullBGlist = list()\n",
    "    for u in HDunitList[t]:\n",
    "        fullBGlist += unitTractList[u]\n",
    "    remainingSet, currPop = set(fullBGlist),fullPop\n",
    "    sheddableSet, shedSet = set(),set()  #sheddable are eligible, while shed HAVE been shed\n",
    "    for b in unitTractList[splitUnitNo[t]]:\n",
    "        nonHDnbrSet = set(bgNbrs[b]).difference(remainingSet)\n",
    "        if len(nonHDnbrSet) > 0:\n",
    "            unbroken, noEnclave, sList, eList = enclaveCheck(list(remainingSet.difference({b})) ,bgNbrs)\n",
    "            if unbroken and noEnclave:\n",
    "                sheddableSet.add(b)\n",
    "    print(t,len(sheddableSet),\"starter sheddableSet length\")\n",
    "    while len(sheddableSet) > 0 and currPop > maxDistrictPop:\n",
    "        shedList = list(sheddableSet)\n",
    "        shedDist = [tractCP[b].distance(HDCP[t]) for b in shedList]\n",
    "        shedBG = shedList[shedDist.index(np.max(shedDist))]   \n",
    "        currPop -= tractPop[shedBG]\n",
    "        sheddableSet.remove(shedBG)\n",
    "        remainingSet.remove(shedBG)\n",
    "        shedSet.add(shedBG)\n",
    "        for b in set(bgNbrs[shedBG]).intersection(remainingSet) :\n",
    "            unbroken, noEnclave, sList, eList = enclaveCheck(list(remainingSet.difference({b}) ) ,bgNbrs)\n",
    "            if unbroken and noEnclave:\n",
    "                sheddableSet.add(b)  #b could already be in the sheddable set, but that's OK\n",
    "            else:\n",
    "                sheddableSet = sheddableSet.difference({b})  #protects against newly created enclaves\n",
    "\n",
    "        splitBGlist[t] = list(remainingSet)\n",
    "    if len(hasSplitUnit) < 20:  #if here prevents massive printing\n",
    "        print(\"  all done assigning split-unit BGs for HD\",t,\"pop/aDP is now\",r3(currPop/aDP))\n",
    "        print(\"  final pop/aDP is\",np.sum([tractPop[b] for b in splitBGlist[t] ]) / aDP )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "91f1020e-0c90-4a0e-8507-ed4e39b0b91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in hasSplitUnit:\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(splitBGlist[t] ,bgNbrs)\n",
    "    print(t,unbroken,noEnclave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4478d9e-7bc1-4c00-9590-32d09ae56142",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "04dd5198-d238-41cc-b57e-a946e3e74adb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "histogram of bg use\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg and SD of blockgroup use are 1.01181 0.05566\n"
     ]
    }
   ],
   "source": [
    "BGuse = [0.]*nTracts\n",
    "finalBGlist = [list() for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    if t in hasSplitUnit:\n",
    "        finalBGlist[t] = splitBGlist[t].copy()\n",
    "    else:\n",
    "        for u in HDunitList[t]:\n",
    "            finalBGlist[t] += unitTractList[u]\n",
    "    for b in finalBGlist[t]:\n",
    "        BGuse[b] += nDistricts * HDweight[t]\n",
    "popBGlist = list()\n",
    "for b in range(nTracts):\n",
    "    if b not in skipList:\n",
    "        popBGlist.append(b)\n",
    "plt.hist([BGuse[b] for b in popBGlist],weights= [tractPop[b] for b in popBGlist],bins=50)\n",
    "print(\"histogram of bg use\")\n",
    "plt.show()\n",
    "bgAvgUse, bgSD = getWeightedAvgAndSD(BGuse,tractPop)\n",
    "print(\"avg and SD of blockgroup use are\",r5(bgAvgUse),r5(bgSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d98ae9cf-2b88-493b-a4a7-a323c342dfd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "finalHDvPop = [np.sum([tractPop[v] for v in finalBGlist[t] ]) for t in range(nHDs)]\n",
    "plt.hist([finalHDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls= \"--\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "59d332e1-2b4b-4e17-90ee-a9c023ee9bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(t,\"doesn't have home unit\",homeU[t],\"in its unit list. Its split muni no is\",splitUnitNo[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "cdb4978f-282d-46e0-9a6e-36a246202be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1415 POINT (-84.66367492465827 39.09723958852362) 30\n",
      "3388 POINT (-83.44862232215976 39.007475718890966) 0\n",
      "3389 POINT (-83.3943251258412 38.791372925620045) 0\n",
      "3390 POINT (-83.35221589264849 38.6810807840711) 0\n",
      "3391 POINT (-83.65058094175778 38.70433772554564) 0\n",
      "3392 POINT (-83.37940313494411 38.90472219903807) 0\n",
      "3393 POINT (-83.51230756890226 38.72102002730925) 0\n",
      "3394 POINT (-83.5058963861911 38.88964158965604) 0\n",
      "3395 POINT (-83.33124635483678 38.98616200702619) 0\n",
      "3396 POINT (-83.30989836011882 38.78104843180336) 0\n",
      "3397 POINT (-83.57610165689206 38.93745886379348) 0\n",
      "3398 POINT (-83.54827419810209 38.97844662944265) 0\n",
      "3399 POINT (-83.63333754790109 38.805611065941626) 0\n",
      "3400 POINT (-83.65399346913644 38.94362026397239) 0\n",
      "3401 POINT (-83.63874771505972 38.963516924034046) 0\n",
      "3402 POINT (-83.53128497841243 38.79777076656218) 0\n",
      "3403 POINT (-83.54470388628717 38.815538511527805) 0\n",
      "3404 POINT (-83.553581204966 38.78717413332099) 0\n",
      "3405 POINT (-83.47390235562237 38.80636250496333) 0\n",
      "7676 POINT (-83.61186887924407 38.88740931654406) 0\n",
      "7677 POINT (-83.5996330401438 38.6944938444674) 0\n",
      "7678 POINT (-83.40982041808087 38.94629139817056) 0\n"
     ]
    }
   ],
   "source": [
    "vtdCountyNo = [-999]*nHDs\n",
    "for v in popHDlist:\n",
    "    for c in range(nCounties):\n",
    "        if countyGeom[c].contains(HDCP[v]):\n",
    "            vtdCountyNo[v] = c\n",
    "            break\n",
    "    if vtdCountyNo[v] <= 0:\n",
    "        countyDist = [countyGeom[c].distance(HDCP[v]) for c in range(nCounties)]\n",
    "        vtdCountyNo[v] = countyDist.index(np.min(countyDist))\n",
    "        print(v,HDCP[v],vtdCountyNo[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ba05a4e1-e62b-489d-8052-41604346d479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final save in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"final save in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":homeC } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"COI1850patchedUnits.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ca4f5b88-ff08-46ce-92d9-f4b71054a5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now write the final bg lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"now write the final bg lists to a file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDbgList\":finalBGlist,\n",
    "                      \"centroid x\":HDCPx, \"centroid y\":HDCPy,\"countyNo\":vtdCountyNo,\"splitUnitNo\":splitUnitNo} )\n",
    "outname = STATE+str(int(nHDs))+\"COI1850legislPatchedBGs.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)  #muni-based complete 11-11-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c42effd1-7ad5-49f1-b021-536f90ece7c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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